>> THANK YOU EVERYBODY. WELCOME
TO BIG DATA APPROACHES FOR
NOVEL MECHANISTIC INSIGHTS ON
DISORDERS OF SLEEP AND
CIRCADIAN RHYTHMS.
I AM DOCTOR
LAWRENCE BAIZER, PROGRAM
OFFICER THE NATIONAL CENTER OF
SLEEP RESEARCH AND TO ORGANIZE
THIS EVENT WE HAD GREAT
PRESENTATIONS AND WE TALKED
ABOUT RESOURCES FOR SLEEP
ANALYSIS AND HAD A ROBUST PANEL
DISCUSSION AT THE END.
THE WORKSHOP CHAIRS WILL
PROVIDE A MORE DETAILED
SUMMARY. AS WELL AS A BRIEF
OVERVIEW OF TODAY'S SESSIONS IN
A COUPLE OF MINUTES AND WE
COVERED MOST OF THE
HOUSEKEEPING ISSUES, QUESTIONS
AND ANSWERS. THE SCHEDULE IS
FOR 15 MINUTE TALKS WITH FIVE
MINUTES FOR QUESTIONS AND
ANSWERS AT THE END. FOR EACH
TALK AND QUESTION AND ANSWERS
IS SUPPOSED TO BE 20 MINUTES
BUT WE NEED TO WATCH THE TIME.
SO IF WE HAVE TIME AT THE END
OF THE PRESENTATIONS, WE COULD
MAYBE HAVE TIME FOR ONE OR TWO
QUESTIONS. BUT THERE IS ALSO A
LOT OF Q&A SESSIONS AT THE END
OF EACH SESSION. EACH SESSION
IS 2-4 TALKS. IT WOULD BE MORE
FEASIBLE TO ASK QUESTIONS THEN.
BUT SEND THEM IN, AND THE
MODERATOR CAN POST QUESTIONS
WHENEVER IT IS MOST CONVENIENT
THE WORSHIP TEAM WILL PROVIDE
WARNINGS AT 18 MINUTES.
SO MANY THANKS TO THE WORKSHOP
COCHAIRS, DR. SHAUN PURCELL
FROM HARVARD, LAUREN HALE,
STONY BROOK MEDICINE. AND AS
WELL WITH THE NIH PROGRAM
COLLEAGUES WHO PARTICIPATED IN
THIS WORKSHOP AND THANK YOU FOR
THE TEAM FOR THEIR EFFORTS IN
MAKING THIS EVENT POSSIBLE.
THANKS AGAIN FOR YOUR
ATTENTION. OVER TO THE
WORKSHOP COCHAIRS DR. PURCELL
AND HALE FOR THE REEFER MARKS
FORWARD GET STARTED.
>> DR. PURCELL: HELLO WELCOME
TO DAY 2. FOR THOSE WHO MISSED
YESTERDAY'S SESSION, THEY WILL
BE AVAILABLE ONLINE SO YOU CAN
GO TO THAT AT SOME FUTURE DATE.
BRIEFLY, I THINK WE HEARD ABOUT
THE RICH VARIETY OF DATA
RESULTS AND TOOLS AVAILABLE FOR
IN-DEPTH SLEEP ANALYSIS AND
SEVERAL EXAMPLES OF
APPLICATIONS.
ONE CLEAR THING IS IMPORTANT OF
DIFFERENT MODALITIES OF DATA,
SLEEP. BUT PERHAPS WITH THE
QUALIFICATION THAT THESE DO NOT
NECESSARILY NEED TO BE
CONCURRENTLY MEASURED, AS PER
THE TRADITIONAL PSG. RATHER
THE VALUE OF DECOUPLING
DIFFERENT SENSES WAS RAISED FOR
POTENTIAL MORE EFFICIENT AND
GETTABLE STUDIES. AND I THINK
THESE THINGS WERE ALREADY
ECHOED. WITH TOOLS FOCUSED ON
THE PSG, AND WITH THE DIVERSITY
OF TALKS PRESENTED.
SECOND AND SOMEWHAT
HAND-IN-HAND WITH THIS, I THINK
THERE WAS ENTHUSIASM FOR THE
QUALIFICATIONS, SLEEP SIGNALS
WHATEVER THEIR SOURCE IN A WAY
THAT IS NOT NECESSARILY QUITE
SO YOKED TO TRADITIONAL R&K
CLASSIFICATIONS. I THINK IT IS
APPEALING IN MANY WAYS
SPECIALLY DOUBT AND AMOUNT OF
THE FOCUS IS ON METRICS PERHAPS
WITH GREATER CROSS SPECIES
TRANSFERABILITY WHERE R&K
HEURISTICS DON'T APPLY IN ANY
CASE.
I'VE SEEN THESE TYPES OF IDEAS
PERCOLATE THROUGH THE
LITERATURE GOING BACK TWO
DECADES. I THINK THE INCREASE
IN DATA AND ANALYTICS WE ARE
WITNESSING NOW MAY BE A GAME
CHANGER IN HELPING TO PUSH
FORWARD SOME OF THESE EFFORTS
FOR MORE EMPIRICALLY GROUNDED
MEASUREMENTS OF SLEEP, TO
COMPLEMENT TRADITIONAL STAGING.
WE HAD A PANEL DISCUSSION IS
NOTED THAT CONTINUED SOME OF
THESE THEMES, AND SOME LARGER
QUESTIONS LIKE WHAT SLEEP
MEASURES ARE ESSENTIAL AND WHAT
IS THE BEST WAYS TO OBTAIN THEM?
AND ALSO UNDERSCORE THE
IMPORTANCE OF NON-THEOLOGICAL
FACTORS IN MEASURING SLEEP,
ENVIRONMENTAL, SOCIAL
INFLUENCES AND SUBJECTIVE
PATIENT REPORTED OUTCOMES AND I
THINK THESE ARE SOME OF THE
THEMES WILL GO INTO TODAY IN
MORE DEPTH IN THIS RECESSION SO
I THINK WILL TURN IT OVER TO MY
COCHAIR DR. HALE WHO WILL I
BELIEVE INTRODUCED TODAY'S
SESSIONS THANK YOU.
>> DR. HALE: THAT IS RIGHT
TODAY WE HAVE THREE REALLY
EXCITING PANELS. THE FIRST
WILL BE LOOKING AT THE EFFECTS
OF SLEEP DISORDERS ON ADVERSE
HEALTH OUTCOMES, USING BIG DATA.
THE SECOND, LOOKING AT IT OVER
THE LIFE COURSE WHICH IS OF
INTEREST TO ME. AND FINALLY I
THINK ALL OF US ARE INTERESTED
IN WHAT THE LARGER IMPLICATIONS
ARE FOR POPULATION, WELL-BEING,
THE ENVIRONMENTAL INFLUENCES,
THE ETHICAL INFLUENCES ISSUES.
LET ME INTRODUCE OUR THREE
MODERATORS FOR TODAY. HERE IS
WHAT THEY ARE. SESSION 3 IS
MODERATED BY INNA BELFER,
PROGRAM OFFICER FOR THE
NATIONAL CENTER FOR INTEGRATED
HEALTH, DEPUTY BRANCH CHIEF
FOR BASIC AND MECHANISTIC
RESEARCH BRANCH. THE SESSION 4
IS MODERATED BY TODD HORWITZ
PROGRAM OFFICER AT NCI AND IN
THE BEHAVIORAL SCIENCES AND
BEHAVIORAL RESEARCH PROGRAM IN
SESSION 5, THE LASTING FOR THE
DAY MODERATED BY DANISH LAZAR
(PHONETIC) PROGRAM OFFICER IN
THE OFFICE OF BEHAVIOR IN
SOCIAL SCIENCE RESEARCH AND THE
OFFICE OF THE DIRECTOR.
SO WITHOUT FURTHER ADO, LET'S
GET STARTED. I AM SUPER EXCITED
FOR TODAY.
>> THANK YOU SO MUCH LAUREN.
HELLO EVERYBODY I AM VERY
EXCITED TO WELCOME EVERYBODY
FOR SESSION 3. SO MY NAME IS
INNA BEIFER, FROM THE NATIONAL
CENTER FOR INTEGRATED HEALTH
AND COMPLEMENTARY HEALTH, IN
SLEEP RESEARCH ESPECIALLY IN
THE CONTEXT OF COMPLEMENTARY
AND INTEGRATED APPROACHES TO
SLEEP DISORDERS AND ALSO TO THE
EFFECTS OF SLEEP DISORDERS. SO
THIS PARTICULAR SESSION IS VERY
IMPORTANT TO US.
EXAMPLES OF APPLICATIONS OF BIG
DATA APPROACHES, TO CORRECT
SLEEP DISORDERS OUR FIRST
SPEAKER IS DOCTORDIEGO MAZZOTTI.
ASSISTANT PROFESSOR IN THE
DIVISION OF MEDICAL INFORMATICS
AT THE UNIVERSITY OF KANSAS
MEDICAL CENTER. HE USES BIG
DATA SETS, CULMINATION OF
COMPETITION WILL METHOD IN
MACHINE LEARNING TO STUDY SLEEP
DISORDERS.
AND HE IS CURRENTLY FOCUSING ON
THE APPLICATION OF INNOVATIVE
METHODS TO THE ANALYSIS OF ...
BEHAVIORS AND GENETICAL DATA ON
SLEEP DISORDERS TO UNDERSTAND
HOW THEY CAN BE TRANSLATED INTO
CRITICAL KNOWLEDGE AND INTEREST
APPLICATIONS THAT CAN ADVANCE
HEALTHCARE. THE TALK IS ON
DISSECTING (INDISCERNIBLE)
SLEEP APNEA.
>> THANK YOU DR. BAIFER FOR
YOUR INVITATION TO PARTICIPATE
IN THIS WORKSHOP I'M GOING TO
BE TALKING A LITTLE BIT ABOUT
SOME OF THE WORK WE HAVE BEEN
DOING OVER THE LAST SEVERAL
YEARS AND TRYING TO UNDERSTAND
HETEROGENEITY OF OBSTRUCTIVE
SLEEP APNEA, A HIGHLY PREVALENT
SLEEP DISORDER.
A LOT OF WHAT WE COULD
CHARACTERIZE OVER THE LAST FEW
YEARS RELATES TO HOW COMPLEX
THE DISEASE IS. AND HOPING TO
TALK A LITTLE BIT ABOUT SOME OF
THE WORK THAT WE HAVE BEEN
DOING AND HOW WE CAN UNDERSTAND
THE RESEARCH. NEXT SLIDE PLEASE.
SO THERE ARE MANY MECHANISMS,
IF WE LOOK AT WHAT SLEEP DOES
TO A PATIENT THAT EXPRESSES THE
DISEASE, THAT COULD BE
IMPORTANT DETERMINANTS OF
CARDIOVASCULAR VESSEL AT RISK.
THIS REVIEW IS AN ARTICLE
DISCOVERING THE PHYSIOLOGICAL
COMPONENTS FOR PATIENTS THAT
HAVE EXPRESSED CPAP SOME OF
THOSE ARE CURRENTLY BEING
CAPTURED BY MORE CONVENTIONAL
MATRIX SUCH AS THE APNEA
HYPOXIA INDEX INTERACTION,
AROUSALS AND OXYGEN SATURATION.
OVER THE LAST FEW YEARS THERE
WAS A LOT OF INVESTMENT IN
TRYING TO IDENTIFY BIOMARKERS
AND HOW THEY COULD BETTER
RELATE AND BETTER EXPLAIN SOME
OF THE POTENTIAL RISKS OF
OBSTRUCTIVE SLEEP APNEA ON
CARDIOVASCULAR DISEASES AND
SOME OF THOSE MECHANISMS ARE
EXPLAINED IN THIS FIGURE, SUCH
A SYMPATHETIC ACTIVATION AND
OTHERS WHICH PUTS THE PATIENT
AT GREATER RISK.
ONE COMPONENT THAT SOMETIMES
GETS FORGOTTEN AND YESTERDAY
PRESENTATION REPORTED ON HOW
THE CLINICAL PRESENTATION OF A
PATIENT WITH SLEEP APNEA CAN
ALSO BE FORMATIVE, THE
FRAMEWORK OF UNDERSTANDING THE
HETEROGENEITY OF SYMPTOM
PRESENTATION WHICH HAS HELPED
US TO UNDERSTAND THAT THE
CLINICAL IMPACT OF OBSTRUCTIVE
SLEEP APNEA. NEXT SLIDE PLEASE.
WITHOUT REGARD IT'S BEEN 10
YEARS AND THE FIRST STUDY WHICH
WAS PUBLISHED... A STUDY THAT
DESCRIBED THE SYMPTOMS OF
OBSTRUCTIVE SLEEP APNEA AND THE
METHOD THAT IS COMMONLY USED
LIKE A NOT SUPERVISE METHOD TO
IDENTIFY SLEEPING CLASSES OR
LATENT CLASS ANALYSIS, WHICH IN
THAT STUDY, INVESTIGATORS
IDENTIFIED IN NUMBER OF KEY
SYMPTOMS THAT ARE OFTEN
REPORTED BY PATIENTS WITH SLEEP
APNEA. AND THEY USED THIS
METHOD TO TRY TO FIND AND TRY
TO DISSECT AND UNDERSTAND THE
HETEROGENEITY OF THOSE SYMPTOMS
AND MINIMIZE THOSE THREE MAJOR
SYMPTOM CLUSTERS. WHICH ARE THE
DISTURBED SLEEP, MINIMALISM
SYMPTOMATIC IN EXCESS OF THE
SLEEPING IN THE FIRST STUDY WAS
PUBLISHED IN ICELAND, THE FIRST
TIME THAT HAS BEEN DESCRIBED.
AND SINCE THEN ALL OF THE OTHER
STUDIES AROUND THE GLOBE,
PARTICULARLY AROUND THE
FOLLOW-UP STUDIES FROM THE
SLEEP APNEA GLOBAL
INTERDISCIPLINARY CONSORTIUM
HAD THE ABILITY TO REPLICATE
THE EXISTENCE OF THOSE SUBTYPES
IN A MUCH BROADER AND MORE
DIVERSE POPULATION. WITH SOME
DIFFERENCES IN HOW OFTEN THEY
APPEAR. BUT MORE OR LESS
GIVING US THE SAME CONCEPT OF
THOSE THREE MAJOR SYMPTOM
CLUSTERS. NEXT SLIDE PLEASE.
ONE OF THE THINGS THAT REALLY
HELPED US UNDERSTAND WHETHER
UNDERSTANDING THOSE CLINICAL
SUBTYPES ARE RELEVANT, WAS THE
STUDY THAT WE CONDUCTED ON THE
HEALTH STUDY WHICH ONLY TO BE
ABLE TO TO BE CONDUCTED DUE TO
SOME OF THE RESOURCES THAT HAVE
BEEN INVESTED, PUTTING TOGETHER
THIS IN AMAZING REPOSITORY
WHICH ALLOWED ME AS AN EARLY
CAREER INVESTIGATOR TO TRY TO
UNDERSTAND, HOW TO USE DATA
THAT IS AVAILABLE AND CAN BE
UTILIZED FOR SECONDARY
RESEARCH. AND IN THIS CASE WE
WERE ABLE TO THEN DESCRIBE
THOSE SUBTYPES IN THE COMMUNITY
BASE CLINICAL POPULATIONS WHICH
ARE THE BEST STUDIES WE
IDENTIFY THOSE FOUR SUBTYPES,
THE THREE MAIN ONES THAT WERE
DESCRIBED ORIGINALLY, DISTURBED
SLEEP, WITH PATIENTS GREATER
REPORT OF INSOMNIA. MINIMAL IS
SYMPTOMATIC, EXCESSIVELY SLEEPY
AND MODERATELY SLEEPY AND WHAT
WE CAN DESCRIBE IN THE
SELF-STUDY IS THAT WE SEEM TO
HAVE LONGITUDINAL DATA IN
INCIDENTS OR EVENTS.
WE COULD SEE WHICH OF THE
SUBTYPES WILL BE ASSOCIATED
WITH GREATER INCIDENCE OF
CARDIOVASCULAR DISEASE. AND WE
OBSERVED THAT THE EXCESSIVE
SLEEP SUBTYPE WAS THE ONE WITH
GREATER INCIDENCE OF THOSE
EVENTS. THAT WAS TRUE WHEN
COMPARED BOTH TO THOSE WITHOUT
SLEEP APNEA, BUT ALSO ACROSS IN
BETWEEN ALL THE OTHER SUBTYPES
WHEN LOOKING AT THOSE WITH
MODERATE/ SEVERE SLEEP APNEA
WHICH SUGGESTS THAT AS DR.
PURCELL INDICATED IN THE
BEGINNING LOOKING AT THE
PHYSIOLOGICAL PARAMETERS WE CAN
OBSERVE WE CAN UNDERSTAND A LOT
OF CLINICAL RELEVANCE OF THE
METRICS, OR OF THOSE
EXPRESSIONS OF SYMPTOMS OF
PATIENTS THAT CAN HELP US
UNDERSTAND. NEXT SLIDE PLEASE.
SO THOSE FINDINGS WERE THEN
FURTHER REPLICATED BY MANY
OTHER STUDIES AROUND THE GLOBE
INCLUDING CLINICAL POPULATIONS
IN CHILE, COMMUNITY POPULATIONS
AMONG HISPANICS AND LATINOS IN
THE UNITED STATES, CLINICAL
POPULATIONS IN CANADA. AND I
THINK THERE WAS ONE STUDY IN
FRANCE, WHERE THE ASSOCIATE WAS
NOT WHAT THEY FOUND. AND I
THINK THAT JUST SHOWS SOMETIMES
HOW HETEROGENEOUS THE DISEASES
AND HOW WE NEED TO BETTER
UNDERSTAND OR EVEN BETTER
CHARACTERIZE SOME OF THOSE
PARAMETERS ARE PERHAPS TRY TO
COME UP WITH BETTER WAYS TO
STANDARDIZE HOW SOME OF THOSE
ASSESSMENTS ARE BEING MADE
WHICH I THINK THAT IS ANOTHER
TOPIC THAT WAS BEEN DISCUSSED
YESTERDAY QUITE A LOT, HOW WE
ARE COLLECTING AND REPRESENTING
AND STERILIZING THIS DATA. AND
SOMETIMES WE SEE THAT THE
HETEROGENEITY IN STUDIES THAT
COULD BE PARTIALLY EXPLAINING
HOW TO COLLECT ANSWERS TO THE
QUESTIONS WE ARE ASKING. NEXT
SLIDE PLEASE.
ONE WAY TO TRY TO MINIMIZE AND
HELP STANDARDIZE SOME OF THOSE
PARAMETERS CAN BE DONE BY
PROVIDING LIKE AVAILABLE OPEN
SOURCE TOOLS THAT FOLKS CAN USE
TO REALLY COME UP WITH THOSE
METRICS. AND WE DID THE STUDY
WHICH WE TRIED TO UNDERSTAND
WHAT WAS THE RELATIVE IMPACT OF
SYMPTOM SUBTYPES IN PREDICTING
CARDIOVASCULAR RISK AND WHAT
WAS THE IMPACT OF SOME OF THE
PHYSIOLOGICAL METRICS THAT HAVE
BEEN REPORTED OVER THE LAST
SEVERAL YEARS. AND ALSO BEING
MORE IMPORTANT THAN
CONVENTIONAL MATRIX SUCH AS THE
AJI, SUCH AS THE HYPOXIC
COMPONENT DESCRIBED BY
AZARBAZRZIN ET AL IN 2019.
IS IT HYPOXIC BURDEN OR SYMPTOM
SUBTYPE THAT EXPLAIN MOST OF
THE CARGO VESSEL RISK?
(CORRECTION) CARDIOVASCULAR
RISK?
AND SO AS PART OF THIS
PROCESS... DESCRIBED IN THE
ORIGINAL APPLICATION AND ALSO
BY THE LAB. NEXT SLIDE PLEASE.
FIRST WE TRY TO UNDERSTAND, CAN
WE SEE, WHAT ARE THE
DIFFERENCES IN THE HYPOXIC
BURDEN DISTRIBUTION WHEN WE
COMPARE THOSE SYMPTOM SUBTYPES?
NOT SURPRISINGLY AS YOU CAN SEE
IN THE LEFT, FOLKS WITH SLEEP
APNEA, WITH DEFINED HERE AS --
THEY DO HAVE A HIGHER HYPOXIC
BURDEN ACROSS THE BOARD AND WE
SEE THAT THOSE MODERATELY OR
EXCESSIVELY SLEEPING THEY TEND
TO HAVE SLIGHTLY HIGHER. NOT A
DRAMATIC HIGHER EFFECT BUT THEY
TEND TO HAVE SLIGHTLY HIGHER.
THAT IS EVEN MORE APPARENT WHEN
WE LOOK ONLY AT THOSE WITH MORE
ESTABLISHED DISEASE BY
CONVENTIONAL MEANS USING AN AJI
15. SO THERE BE SOME
NONRATIONAL THAT MAYBE HYPOXIC
BURDEN TO BE EXPLIAN SOME OF
THE CARDIOVASCULAR DISEASE.
NEXT SLIDE PLEASE.
WHAT WE DID IS TRY TO REPLICATE
THE ORIGINAL I█NVESTIGATION
BETWEEN BOTH HYPOXIC BURDEN AND
THE SYMPTOMS OBTAINED BY OUR
GROUP AND SEE WHETHER THOSE
THINGS WENT TOGETHER, AND COULD
BETTER EXPLAIN THE
CARDIOVASCULAR DISEASE. AND WE
DID THIS FOR BOTH OUTCOMES,
CARDIOVASCULAR DISEASE
MORTALITY AND MAJOR RISK
CARDIOVASCULAR EVENTS AND WHAT
WE SAW IS THAT THE PENNY ON THE
OUTCOME, DEPENDING FOR
CARDIOVASCULAR MORTALITY
HYPOXIC DEPRIVATION IS A
SIGNIFICANT PREDICTOR DEPENDING
ON THE SUBTYPE AS YOU CAN SEE
IN THE LEFT BOTTOM PART. AND
FOR INSTANCE OF NEW MAJOR
CARDIOVASCULAR EVENTS THE
EXCESSIVE SLEEPY SUBTYPE IS AN
IMPORTANT PREDICTOR EVEN WHEN
YOU CONTROL FOR THOSE CHANGES
IN HYPOXIC BURDEN.
SO IN THE CONTEXT, I THINK BOTH
MEASURES ARE IMPORTANT. AND IT
HIGHLIGHTS THE IMPORTANCE OF
RECOLLECTING BOTH PATIENT
REPORTED OUTCOMES AND SYMPTOM
PRESENTATION AND ALSO SOME OF
THOSE PHYSIOLOGICAL METRICS.
PERHAPS THE ANSWER IN TRYING TO
UNDERSTAND THE HETEROGENEITY IS
IN THIS MULTIMODAL COMPONENT,
WERE PATIENT REPORTED OUTCOMES
IS JUST ANOTHER DATA MODALITY
OF INTEREST. NEXT SLIDE PLEASE.
ONE THING I'M INTERESTED TOO,
IS HOW CAN WE TRANSLATE THOSE
TO USE MORE READILY AVAILABLE
IN CLINICAL PRACTICE? SO WE
KNOW THAT THE APNEA SLEEP SCALE
WAS WIDELY UTILIZED ALTHOUGH
THERE ARE SEVERAL ITERATIONS
ABOUT ITS UTILIZATION AND IT IS
READILY AVAILABLE. IF WE LOOK
AT CLINICAL NOTES OR EHR DATA,
IT TENDS TO BE AVAILABLE ONE
WAY OR THE OTHER SO IT IS EASY
TO ACCESS IF YOU'RE THINKING
ABOUT A LARGER PERSPECTIVE
STUDIES INTEGRATING DATA. BUT
ONE THING THAT WE ASKED IS THE
SCALE SUFFICIENT TO REALLY
EXPLAIN THE ACCESSIBILITY OF
SLEEPINESS USING THE FRAMEWORK
OF THE SYMPTOM SUBTYPES OR NOT?
AS YOU CAN SEE HERE WE COMPARE
THE SCALES ACROSS THE DIFFERENT
FOUR SUBTYPES IN THREE
DIFFERENT COHORTS, THERE IS A
GREATER VARIABILITY. IF YOU ARE
THINKING ABOUT SOME OF THE
CUTOFFS THAT HAVE BEEN USED IN
THE PAST TO EXCLUDE CERTAIN
PARTICIPANTS FROM TRIALS,
LOOKING AT THERAPISTS FOR
OBSTRUCTIVE SLEEP APNEA,
GREATER THAN 15, WE ARE
EXCLUDING LOTS OF PATIENTS OF
THE EXCESSIVE SLEEP SUBTYPE
WHICH ARE PROBABLY AT GREATER
RISK. BUT IT DOES NOT EXPLAIN
EVERYTHING. NEXT SLIDE PLEASE.
SO WE CAME UP WITH A WAY OF
TRYING TO IDENTIFY THE MINIMUM
SET OF SYMPTOMS SOME OF THE
DIFFERENCES WE OBSERVED USING
MAYBE LAST QUESTIONS AND HERE
IS AN EXAMPLE OF LIKE A
DECISION TREE THAT ACTUALLY
PERFORMED PRETTY WELL BASED ON
THE METRICS I REPORTED HERE TO
IDENTIFIED PATIENTS AT RISK.
AND THOSE QUESTIONS ARE NOT
REALLY A MUCH GREATER BURDEN.
THAT IS AN EXAMPLE OF HOW WE
CAN TRY TO SIMPLIFY SOME OF
THOSE MORE CLUSTERING BASES,
THAT CAN BE EASILY
INTERPRETABLE BY CLINICIANS IN
THOSE DIRECTLY INTERACTING WITH
PATIENTS. NEXT SLIDE PLEASE.
SO I WANT TO SWITCH GEARS A
LITTLE BIT AND TALK A LITTLE
BIT ABOUT HOW WE CAN USE SOME
OF THIS INFORMATION TO
UNDERSTAND THERAPIES. ONE OF
THE BIG CHALLENGES THAT WE
OBSERVED TODAY IS ABOUT THIS
GREATER AVAILABILITY OF
CLINICAL DATA, THAT IS ACROSS
THE BOARD. BUT NOT REALLY A LOT
OF GRANULARITY IN TERMS OF
CHARACTERIZING SLEEP DISORDERS.
AND THEN ONE EXAMPLE THAT I
WANT TO KIND OF SHOW HERE
TODAY, IS HOW WE CAN STILL USE
SOME OF THIS LESS GRANULAR, YET
VERY BIG, BIG DATA SET TO HELP
US INFORM THE EFFECTS OF
THERAPIES FOR SLEEP APNEA TO
DRIVE INCENTIVES TO FACILITATE
THE INTEGRATION OF MORE
GRANULAR DATA WE DISCUSSED OVER
THE COURSE OF THIS WORKSHOP
INTO THOSE CLINICAL DATA
REPOSITORIES.
IN THIS STUDY WE LOOKED AT A
VERY LARGE MEDICARE BENEFICIARY
DATA SET, REPRESENTING ALL
INDIVIDUALS GREATER THAN 65
YEARS OLD THAT ARE MEDICARE
BENEFICIARIES ACROSS NINE
STATES IN THE CENTRAL UNITED
STATES. THIS IS PART OF THIS
GREATER COLLABORATIVE WHICH IS
THE (INDISCERNIBLE) NETWORK.
IN THIS STUDY WE WERE
INTERESTED IN TRYING TO
UNDERSTAND WHETHER THE
UTILIZATION OF CONTINUOUS
POSITIVE PRESSURE, BASED ON
CLAIMS, MEDICARE CLAIMS, AND
THE INITIATION AND UTILIZATION
BE ASSOCIATED WITH DECREASED
VASCULAR RISK AND MORTALITY.
IN THAT SENSE WE DEFINED--
BECAUSE IN SOME OF THE
PRESENTATIONS WE DISCUSSED HOW
SOMETIMES CLINICAL QUOTES MIGHT
NOT EXPLAIN SUFFICIENTLY HOW TO
DEFINE A PATIENT.
UNFORTUNATELY SOME OF THOSE
MASSIVE ADMINISTRATIVE
DATABASES DON'T REALLY HAVE ANY
MORE INFORMATION BEYOND THOSE
CLINICAL CODES. SO THEN WE HAVE
TO COME UP WITH PHENOTYPES TO
BETTER CHARACTERIZE THE
PATIENTS AND EVEN THOUGH IT IS
NOT A PERFECT DEFINITION IT IS
BETTER THAN JUST USING ONE CODE
TO SAY THAT SOMEONE HAS SLEEP
APNEA.
SO WE IDENTIFIED A COHORT OF
PATIENTS WITH SLEEP APNEA BASED
ON CLAIMS USING VALIDATED
ALGORITHMS. NEXT SLIDE PLEASE.
AND THEN WE DESIGNED TWO
STUDIES.
THE DESIGN OF THE STUDIES
IMPORTANT TO MENTION BECAUSE
THEY REALLY FOLLOW LIKE A VERY
ROBUST FRAMEWORK OF CAUSAL
REFERENCE WHICH IS REALLY ONE
OF THE BIGGEST CHALLENGES THAT
WE HAVE PARTICULARLY IN THE
FIELD, WHERE WE CANNOT
DETERMINE CERTAIN CAUSAL
EFFECTS, IF YOU ARE NOT DOING A
RANDOMIZED CONTROLLED TRIAL.
HOWEVER THERE ARE MANY METHODS
TODAY CAN MINIMIZE AS MUCH AS
WE CAN SOME OF THE BIASES WE
OBSERVED, AND IT IS ABOUT TIME
FOR US TO START USING SOME OF
THOSE RESOURCES TO ASK SOME OF
THE QUESTIONS THAT WE CANNOT
ANSWER WITH CLINICAL TRIALS.
SUCH AS FOR EXAMPLE WHETHER
PATIENTS WITH SLEEP APNEA USING
CPAP HAVE LOWER CARGO VASCULAR
RISK, WHICH WE CANNOT DO
ETHICALLY. SO WE DID STUDIES,
DID ASSIMILATION WHICH DR.
PURCELL MENTIONED A LITTLE BIT
ABOUT SO WE TRY TO RELATE HOW
TRIALS WOULD HAPPEN BY LOOKING
AT OBSERVATIONAL DATA, BUT TRY
TO MINIMIZE AS MUCH AS WE CAN
ALL THE BIASES WE OBSERVED FROM
LOOKING AT OBSERVATIONAL DATA.
SUCH AS PORTAL TIME BIAS, IN
TRYING TO DO AS MUCH AS YOU CAN
EVEN THOUGH THERE WILL BE A
MEASURE COMPOUNDING.
SO WE LOOKED AT THIS DATABASE
OF 2 MILLION MEDICARE
BENEFICIARIES ABOUT HUNDRED
88,000 ASSOCIATED, IDENTIFIED
AS HAVING SLEEP APNEA BASED ON
THE UPDATED ALGORITHM AND THEN
WE COMPARE THOSE THAT INITIATED
CPAP WITH THE ONES THAT DID NOT
INITIATE CPAP IN ONE STUDY AND
AMONG THOSE THAT INITIATED CPAP
WE LOOKED AT THE THE DIVISION
OF UTILIZATION OF CPAP OVER THE
FIRST YEAR OF UTILIZATION, TO
SEE IF IT WAS ASSOCIATED WITH
MORTALITY AND CARDIOVASCULAR
RISK. NEXT SLIDE PLEASE.
AS YOU CAN SEE HERE FROM THE
OBSERVATIONAL DATA, ROBUST
DESIGN STUDY WE ARE STILL
SUBJECTED TO POTENTIAL MEASURE
CONFOUNDING. WE PERFORM AN
ANALYSIS TO HELP MINIMIZE THIS
AND WE SEE AN ASSOCIATION
BETWEEN CPAP UTILIZATION AND
LOWER MORTALITY AND LOWER
INCIDENCES FOR CARDIOVASCULAR
DISEASES. THE ANALYSIS WAS
CONSISTENT WHEN WE LOOKED
ACROSS DIFFERENT GROUPS,
SUBGROUPS OF THE DATA SET THAT
SUGGEST THE DATA BEING ROBUST.
NEXT SLIDE PLEASE.
AND THEN WE LOOKED AT
UTILIZATION WHICH AGAIN CAN
ONLY BE BASED ON A NUMBER OF
CLAIMS.
BUT ONE THING THAT WAS
INTERESTING BY LOOKING AT THIS
HISTOGRAM, IS THAT THE PATTERNS
OF THE NUMBER OF CLAIMS A
PATIENT HAS WITHIN THE FIRST
YEAR, THEY REFLECT VERY WELL
WITH HOW SOME OF THIS CPAP IS
REVERSED BY MEDICARE. WHERE WE
HAVE LIKE THE FIRST MODE, THE
FIRST BUMP IN THE DISTRIBUTION,
WHERE IT IS MORE OR LESS 3-4
AIMS, WHEN THE PARTICIPANTS
DIDN'T REALLY NEED THE ADHERENT
CRITERIA AND THE BIGGER BUMP
WHEN PATIENTS DO. AND WE LOOKED
AT THE DISTRIBUTION INTO
QUARTILES AND WE SEE A
PROGRESSIVE LOWER RISK OR LOWER
INCIDENCE OF MORTALITY IN MAJOR
RISK OF CARDIOVASCULAR EVENTS
AND ALSO SUGGESTING ROBUST
FINDINGS. NEXT SLIDE PLEASE.
SOLD MESSAGES ABOUT WHAT I JUST
TALKED ABOUT TODAY. I THINK
SLEEP APNEA IS A HETEROGENEOUS
DISEASE. IT SHOULD BE
ADDRESSED AT DIFFERENT LEVELS
INCLUDING SYMPTOM PRESENTATIONS
AS AN ADDITIONAL DATA MODALITY
THEY SHOULD CONSIDER.
THERE NEEDS TO BE A NEED FOR
TOOLS FOR BETTER PHENOTYPING
SLEEP APNEA AND THOSE SHOULD BE
AVAILABLE FOR CLINICAL RESEARCH
ACROSS DIFFERENT DATA
MODALITIES. NOT JUST FOR
SYMPTOMS, FOR PHYSIOLOGY, AND I
THINK THERE IS A LOT OF WORK IN
THIS AREA THAT IS HAPPENING
CURRENTLY.
THERE IS ALSO AN IMPORTANCE OF
THE VERY LARGE MULTI SITE
CRITICAL RESEARCH NETWORKS
CURRENTLY THEY ARE NOT
INTEGRATED WITH SLEEP DATA. I
THINK THERE ARE SOME EFFORTS
OUT THERE THAT WE SAW
YESTERDAY, TRYING TO DO THIS.
BUT IDEALLY WE WANT TO HAVE THE
ABILITY TO BETTER CHARACTERIZE
PATIENTS WITH SLEEP APNEA USE
AND CUTTING AGE WAYS OF
DEFINING THE DISEASE USING THE
RAW SIGNALS, AND WHAT WE TALKED
ABOUT YESTERDAY. BUT THEN LINK
THOSE TWO OUTCOMES IN THE EHR
OR TO THERAPY DATA WHICH IS
VERY GRANULAR. BUT IT BECOMES
CHALLENGING TO INTEGRATE THEM.
MOSTLY HOSTED OF VENDORS OF
CPAP DEVICES AND NOT ONLY CPAP
BUT ALSO OTHER THERAPIES AS
WELL WHICH WE ARE SEEING
DEVELOPED MORE AND MORE.
ANOTHER COMPONENT THAT IS VERY,
VERY IMPORTANT THAN HAS BEEN
VERY UNDEREXPLORED IN THE
CONTEXT OF SLEEP APNEA, IS
WHETHER THE 1990 VARIABILITY
OF THE DISEASE IS NOT A
CONTRIBUTOR TO HETEROGENEITY.
AND THERE ARE SEVERAL STUDIES
WE TRY TO CLASSIFY PATIENTS,
AND CAPTURE STUDIES ABOUT
SOMEONE WHO HAS OR HAS NOT THAT
WE DO NOT KNOW WHETHER THE
VARIABILITY SOMEONE IS
ASSOCIATED OR CAN PREDICT SOME
PHYSIOLOGICAL CHANGES THAT
COULD PUT THEM AT RISK AND SO
THOSE ARE THE AREAS OF INTEREST
THAT OUR GROUP IS WORKING ON.
NEXT SLIDE PLEASE.
>> YOU HAVE TWO MINUTES LEFT.
>> THAT IS MY ACKNOWLEDGMENT
SLIDE. THANK YOU FOR THE
OPPORTUNITY TO TALK TODAY AND I
WOULD LIKE TO ACKNOWLEDGE ALL
OF MY COLLABORATORS. IT IS
REALLY A GREAT PLEASURE TO BE
WORKING WITH THIS GROUP, AND
REALLY COULD NOT DO ANY OF THAT
WITHOUT THEIR SUPPORT AND THEIR
HELP, AND ALL THE FUNDING
AGENCIES AND AGAIN A GREAT
SHOUT OUT TO THE NSR FOR THEIR
HELP IN THIS WORK.
>> THANK YOU FOR YOUR EXCELLENT
PRESENTATION. I DON'T SEE ANY
QUESTIONS IN THE Q&A. SO WE
ARE DELIVERED BEHIND THE
SCHEDULE. I WOULD LIKE TO
INVITE NOW OUR NEXT SPEAKER AND
ALL THE QUESTIONS PLEASE PUT
THEM INTO THE Q&A. WE WILL
HAVE THIS Q&A SESSION FOLLOWING
THE LAST PRESENTATION.
OUR NEXT SPEAKER IS DOCTOR
SOOMI LEE, ASSOCIATE PROFESSOR
IN THE DEPART ENOUGH HUMAN
DEVELOPMENT AND FAMILY STUDIES
AT PENN STATE UNIVERSITY. SHE
DIRECTS THE SLEEP AND HEALTH
LABORATORIES AND HER EFFORTS
ARE TO IMPROVE SLEEP AND
DECREASE STRESS IN ORDER TO
SUPPORT HEALTH. AND HER TITLE
IS SLEEP HEALTH PROFILES, PAIN
AND BIOLOGICAL AND BEHAVIORAL
MECHANISMS. DR. LEE?
>> DR. LEE: THANK YOU FOR THE
INTRODUCTION AND IMITATION.
TODAY I WILL TALK ABOUT MY
PREVIOUS AND RECENT FINDINGS
RELATED TO SLEEP HEALTH AND
CHRONIC PAIN. AND POTENTIAL
BIOLOGICAL AND BEHAVIORAL
MECHANISMS. NEXT SLIDE PLEASE.
CHRONIC PAIN IS PREVALENT,
ACCORDING TO A CDC REPORT, 20%
OF US ADULTS HAVE CHRONIC PAIN.
WITH 8% HAVING HIGH IMPACT
CHRONIC PAIN. MOREOVER, IN
THE NATIONAL HEALTH INTERVIEW
SURVEY, ONE FIFTH OF THE
POPULATION EXPERIENCED FREQUENT
DAILY PAIN. WE OFTEN DO NOT
PAY MUCH ATTENTION TO THE
SYMPTOMS SUCH AS BACK PAIN,
HEADACHE AND OTHER DISCOMFORTS
BECAUSE THEY MAY SEEM LIKE
TRIVIAL
BUT THEY MATTER. ESPECIALLY
WHEN THOSE SYMPTOMS I CAN
RELATE OVER TIME
MY RESEARCH USES MULTIPLE DATA
TO CAPTURE HOW PAIN MANIFESTS
OVER TIME, INCLUDING AGING.
TODAY OUR FOCUS ON STUDIES THAT
WILL TRACK CHANGES OVER
MULTIPLE YEARS AND TIME
CONSTRAINTS.
WE ALSO KNOW THAT PAIN SYMPTOMS
ARE MORE PREVALENT AMONG ADULTS
LIVING IN POVERTY, AND THOSE
WITH LOWER EDUCATION. CREATING
CONCERNS WITH HEALTH
DISPARITIES.
MORE IMPORTANTLY, PAIN MAY
SHARE COMMON MECHANISMS WITH
POOR SLEEP. NEXT SLIDE PLEASE.
FOR EXAMPLE, THERE ARE
GEOGRAPHIC OVERLAPS BETWEEN
HIGH PAIN AND SHORT SLEEP. THE
MAP ON THE LEFT SHOWS MEAN PAIN
SCORES BY STATE MEASURED BY THE
PRODUCT OF THE SELF REPORTING
FREQUENCY AND PAIN
INTERFERENCE. STATES WITH HIGH
PAIN SCORES ARE RED. AND STATES
WITH LOW PAIN SCORES ARE
CURRENTLY BLUE. AND MIDDLE PAIN
IS ON YELLOW.
ON THE RIGHT SIDE YOU SEE THE
PREVALENCE OF SHORT SLEEP
DURATION BY STATE. DARK COLORS
INDICATE STATES WITH HIGH
PREVALENCE OF SHORT SLEEP.
CLEARLY, THERE IS AN OVERLAP
BETWEEN HIGH PAIN AND SHORT
SLEEP.
ARKANSAS, KENTUCKY, MISSOURI,
ALABAMA AND GEORGIA HAVE HIGH
PREVALENCE FOR BOTH. NEXT SLIDE
PLEASE.
HERE, I INCLUDED THE
ANTECEDENTS AND CONSEQUENCES OF
POOR SLEEP AND PAIN, SHOWN IN
PREVIOUS PUBLICATIONS. THE
LIST HERE MAY NOT BE
COMPREHENSIVE BUT THE
LITERATURE REPORTS THE CLOSE,
BIDIRECTIONAL RELATIONSHIPS
BETWEEN POOR SLEEP AND PAIN.
IN MANY CASES, FACTORS
ASSOCIATED WITH SLEEP ARE ALSO
RELATED TO PAIN. FOR BOTH THE
SLEEP AND PAIN, PREVIOUS
STUDIES HAVE SOME LIMITATIONS.
ONE OF THEM IS RELATED TO
MEASUREMENT.
FOR EXAMPLE, ABOUT HALF OF THE
STUDIES ALONG WITH MOST
PREVIOUS STUDIES HAVE
ASSOCIATED INDIVIDUAL SLEEP
VARIABLES WITH HEALTH OUTCOMES.
MOSTLY USING ONE SLEEP
VARIABLE AT A TIME.
MOREOVER, MOST STUDIES HAVE
FOCUSED ON SLEEP DISORDERS.
BUILDING ON THOSE STUDIES, I
WILL SHOW YOU HOW IT POSITIVE
FRAMING, OR PREVENTION APPROACH
FOCUSING ON SLEEP HEALTH CAN
IMPROVE OUR KNOWLEDGE OF THE
IMPORTANCE OF SLEEP ON HEALTH.
NEXT SLIDE PLEASE.
JUST LIKE HEALTH IS NOT THE
ABSENCE OF DISEASE, SLEEP
HEALTH IS ALSO BEYOND HAVING NO
SLEEP DISORDER. AND THERE IS
INCREASING AWARENESS THAT SLEEP
HEALTH NEEDS TO BE UNDERSTOOD
BY MULTIPLE DIMENSIONS.
SPECIFICALLY DOCTOR DANIEL
BUYSSE SUGGESTED THAT THESE
DIMENSIONS ARE CRITICAL FOR
HEALTH I AM NOT GOING TO GO
OVER THESE DIMENSIONS BUT TO
MENTION SOME REGULARITY, HOW
REGULAR YOU ARE IN THE TIMING
AND QUANTITY OF THE SLEEP.
ALERTNESS. HOW ALERT YOU ARE
DURING DAYTIME? HAVING LESS
SLEEPINESS, AND TAKING FEWER
NAPS. AND EFFICIENCY. WHETHER
YOU CAN FALL ASLEEP IN A
RELATIVELY SHORT PERIOD OF
TIME. AND NOT WAKING UP IN THE
MIDDLE OF THE NIGHT OR TOO
EARLY IN THE MORNING.
IMPORTANTLY, THESE SLEEP
DIMENSIONS ARE STATISTICALLY
DEPENDENT. THEY ARE INFLUENCED
ON HOW HEALTH IS OFTEN
INTERACTIVE.
FOR EXAMPLE, PREVIOUS RESEARCH
RECORDED SHORT-TERM SLEEP
COMBINED WITH POOR SLEEP
QUALITY, ASSOCIATED WITH HIGHER
RISK HYPERTENSION, DIABETES AND
HEART DISEASE. NEXT SLIDE
PLEASE.
MY COLLABORATIVE WORK FUNDED BY
NIH EXAMINED SLEEP HEALTH
PROFILES THAT MEASURE HOW MANY
AND WHICH TYPES OF SLEEP
PROBLEMS COOCCUR WITHIN THE
VISUALS. AND LINK TO DIVERSE
HEALTH AND OUTCOMES.
THESE TWO APPROACHES REQUIRED
USING DIFFERENT METHODS. FOR
EXAMPLE HOW MANY APPROACHES USE
A COMPOSITE SCORE. BINARY
INDICATORS THAT ARE OPTIMAL OR
SUBOPTIMAL OF SLEEP.
WHICH TYPE OF APPROACH USES A
PERSON- CENTERED METHODOLOGY?
SUCH AS LATENT CLASS ANALYSIS
OR LATENT PROFILE ANALYSIS TO
IDENTIFY (INDISCERNIBLE) OF
HEALTH THAT ARE COMMON IN
SPECIFIC POPULATION. I WILL
SHOW YOU SOME OF MY WORK USING
THESE TRUE APPROACHES. NEXT
SLIDE PLEASE.
FIRST, WHICH TYPE OF APPROACH?
USING MULTIDIMENSIONAL SLEEP
DIMENSIONS, OUR PREVIOUS WORK
IDENTIFIED (INDISCERNIBLE)
HEALTH PHENOTYPES, IN MIDDLE
LIFE ADULT. THIS FIGURE SHOWS
THREE COMMON SLEEP PHENOTYPES
IDENTIFIED AT BASELINE.
ON THIS RADAR PLOT, LOCATED
CLOSER TO THE CENTER INDICATES
BETTER SLEEP. THE FIRST
PHENOTYPE, POOR SLEEPERS. THE
GREEN SOLID LINE. THEY WERE
CHARACTERIZED BY POOR SLEEP
ACROSS ALL DIMENSIONS.
THE SECOND PHENOTYPE, CATCH UP
SLEEPERS OR IRREGULAR SLEEPERS,
THE BLUE DOTTED LINE, WERE
CHARACTERIZED BY HIGH NAP
FREQUENCY AND IRREGULAR SLEEP
SUCH AS (INDISCERNIBLE) SLEEP
COMPARED TO SHORT SLEEP.
THE THIRD PHENOTYPE, INSOMNIA
SLEEPERS, IN THE ORANGE LINE,
WERE CHARACTERIZED BY HIGH
INSOMNIA SYMPTOMS, SHORT SLEEP
DURATION, LONG SLEEP LATENCY,
AND SOMEWHAT IRREGULAR SLEEP,
ALL OF WHICH MAPPED ONTO
CLINICAL INSOMNIA SYMPTOMS.
NEXT.
THESE PHENOTYPES WERE
REPLICATED APPROXIMATELY ONE
DECADE LATER, WITHIN THE SAME
PARTICIPANTS. AS YOU SEE HERE,
THE SHAPE OF EACH PHENOTYPE WAS
VERY SIMILAR ACROSS THE TWO
TIME POINTS. NEXT.
WE HAVE SHOWN THAT
MULTIDIMENSIONAL HEALTH
PHENOTYPES ARE GOOD MARKERS OF
HEALTH. FOR EXAMPLE IN THE
STUDY WE FOUND THAT THESE
PHENOTYPES DIVERTING CHRONIC
PHYSICAL CONDITIONS; IRREGULAR
OR CATCH UP SLEEPERS REPORTED
25% MORE CHRONIC CONDITIONS
THAN GOOD SLEEPERS AND INSOMNIA
SLEEPERS REPORTED ALMOST TWICE
AS MANY CHRONIC PHYSICAL
CONDITIONS, COMPARED TO GOOD
SLEEPERS. NEXT SLIDE PLEASE.
THIS IS ANOTHER STUDY SHOWING
SLEEP HEALTH PHENOTYPES, ALSO
ASSOCIATED WITH THE RISK OF
HEART DISEASE, WHICH INVOLVES
CHEST PAIN. SPECIFICALLY,
MEMBERSHIP IN THE INSOMNIA
SLEEPER PHENOTYPE IS ASSOCIATED
WITH 50% HIGHER RISK OF HEART
DISEASE COMPARED TO GOOD
SLEEPERS.
AND THE FINDINGS WERE
REPLICATED ACROSS THE TWO
INDEPENDENT SAMPLES OF THE
MIDUS. NEXT SLIDE PLEASE.
MY CURRENT R01, TITLED SPAI,
MEASURES HOW CHRONIC PAIN IS
MEDIATED BY SOCIAL LEFT OUT AND
INFORMATION IN THREE DIFFERENT
COHORTS OF ADULTS. THIS IS TO
DO SECONDARY DATA ANALYSIS.
WE WERE EXAMINING ACTIVE SOCIAL
LIFE AS A BEHAVIOR MECHANISM.
AND SYSTEMIC INFLAMMATION IS A
BIOLOGICAL MECHANISM. WE HAVE
JUST STARTED ANALYZING DATA TO
ADDRESS AIM 1.
DURING THE REST OF MY TALK I
WILL SHOW YOU FINDINGS OF AIM 1
USING HOW MANY APPROACH IN A
(INDISCERNIBLE) SAMPLE.
I WILL ALSO SHOW YOU MY
PREVIOUS FINDINGS RELATED TO
THESE BEHAVIORAL AND
INFLAMMATORY MECHANISMS. NEXT.
SLEEP HEALTH COMPOSITE SCORE
WAS MEASURED BY BOTH THE
SELF-REPORT AND
(INDISCERNIBLE), RE-CREATING
THE BINARY INDICATORS WHICH
REPRESENT POOR SLEEP.
WE SUMMED UP THE BINARY
INDICATORS TO RECONSTRUCT A
COMPOSITE SCORE OF SLEEP HEALTH
SUCH AS HIGHER SCORE
REPRESENTED MORE SLEEP HEALTH
PROBLEMS.
IN A SAMPLE OF OLDER MEN IN
MROS, PARTICIPANTS HAD 1.4
SLEEP HEALTH PROBLEMS ON
AVERAGE. NEXT.
WE USED THE DEPENDENT MODELS TO
TEST POTENTIAL BIDIRECTIONAL
ASSOCIATIONS BETWEEN BACK PAIN
AND SLEEP HEALTH. HERE BLACK
SOLID LINES REPRESENT
AUTOREGRESSIVE PATH, SUCH THAT
BACK PAIN TIME 1 AND BACK PAIN
TIME 2. AND HAVING ANY BACK
PAIN AT TIME 1 PREDICTED MORE
SLEEP HEALTH PROBLEMS AT TIME
2.
THIS WAS FOUND AFTER
CONTROLLING FOR EXTENSIVE
COVARIATES, INCLUDING SOCIAL
DEMOGRAPHICS, SLEEP DISORDER
DIAGNOSIS, BMI, HEALTH
BEHAVIORS, CHRONIC CONDITIONS,
MEDICATION USE AND SOMEONE.
NEXT.
RESULTS WERE CONSISTENT FOR
THOSE WHO HAD PERSISTENT BACK
PAIN, WHICH REFERS TO THREE
CONSECUTIVE REPORTS OF PAIN
OVER ONE YEAR.
WE ALSO TESTED WITH THE BACK
PAIN SEVERITY AND ACTIVITY
LIMITING BACK PAIN. RESULTS
WERE CONSISTENT. NEXT.
THIS IS FROM A PREVIOUS
PUBLICATION USING BASELINE MROS
SAMPLES, WHICH SHOWS THAT
OSTEOARTHRITIS, POOR SLEEP
HEALTH, AND INFLAMMATION MAY GO
TOGETHER. WE FOUND THAT OLDER
MEN WITH OSTEOARTHRITIS OR OA
HAD A SIGNIFICANTLY POOR SLEEP
HEALTH. IN THIS STUDY WE USED
A COMPOSITE SCORE OF SLEEP
HEALTH.
POORER SLEEP HEALTH WAS IN TURN
ASSOCIATED WITH 16% HIGHER ODDS
OF ELEVATED CRP, AS WELL AS 12%
HIGHER ARTS OF ELEVATED IL-6.
NOTE THAT THERE WERE NO DIRECT
ASSOCIATIONS OF OA WITH THIS
INFLAMMATORY BIOMARKERS.
THUS OLDER MEN WITH OA DO NOT
NECESSARILY HAVE A HIGHER RISK
OF INFLAMMATION. BUT WHEN THEY
HAD THE POORER SLEEP HEALTH THE
RISK OF INFLAMMATION WAS
ELEVATED.
THE RESULTS MAY BE RELATED TO
POTENTIAL JOINT PAIN ASSOCIATED
WITH OA WHICH MAY HAVE BEEN
ASSOCIATED WITH POOR SLEEP AT
NIGHT. NEXT.
I WANT TO BRIEFLY TOUCH ON
POTENTIAL BEHAVIORAL
MECHANISMS. THERE IS UNDER
RECOGNIZED EVIDENCE THAT
PHYSICAL PAIN IS AFFECTED BY
ADVERSE SOCIAL EXPERIENCES.
FOR EXAMPLE, IN A CLINICAL CASE
STUDY, A PATIENT WITH
INSENSITIVITY TO PAIN REPORTED
FEELING PHYSICAL PAIN FOR THE
FIRST TIME SHORTLY AFTER THE
UNEXPECTED DEATH OF A YOUNGER
SIBLING.
THE RESEARCH DEMONSTRATED THAT
THE EXPERIENCES OF SOCIAL
EXCLUSIVE, PREDOMINANTLY
ACTIVATE THE ACC AND AI,
REASONS KNOWN TO HAVE AN
OVERALL DISTRESSING EXPERIENCE
OF PHYSICAL PAIN.
IN ADDITION, SEVERAL LINES OF
RESEARCH HAVE SHOWN THAT
EXPERIENCES OF NEGATIVE, SOCIAL
EVALUATION, RESECTION OF A
ROMANTIC PARTNER AND
BEREAVEMENT ARE RELATED TO
ACTIVITY IN THE DACC AND AI.
GIVEN THE SHARED MECHANISM
TWEEN SOCIAL PAIN AND PHYSICAL
PAIN WE WERE CURIOUS WHETHER
LOWERING SOCIAL PAIN CAN REDUCE
CHRONIC PHYSICAL PAIN. NEXT.
NEXT SLIDE PLEASE.
THIS SHOWS THAT SOCIAL PAIN
MEASURED BY LONELINESS AND
SOCIAL ISOLATION ARE CORRELATES
OF PHYSICAL PAIN, AND MEASURED
BY PAIN INTERFERENCE IN MIDUS.
AS YOU CAN SEE HERE WHEN SOCIAL
ISOLATION IS HIGHER, AND
LONELINESS IS HIGHER, THE
ESTIMATED PAIN INTERFERENCE IS
ALSO HIGHER. I THINK THIS IS
SUCH A STORY THAT ADDRESSING
SOCIAL PAIN MAY HELP REDUCE THE
CHRONIC PHYSICAL PAIN. NEXT
SLIDE PLEASE.
AS EXPECTED WE FOUND THAT IN
MIDUS EXAMPLE LONELINESS WAS
ASSOCIATED WITH CHRONIC PAIN
OVER TIME. SPECIFICALLY
INDIVIDUALS WITH HIGHER
LONELINESS AT BASELINE. AND
THOSE WHO INCREASED LONELINESS
OVER TIME, HAD HIGHER ODDS OF
HAVING ANY CHRONIC PAIN,
GREATER PAIN INTERFERENCE AND
MORE PAINT LOCATIONS AT
FOLLOW-UP.
MOREOVER, THERE WAS EVIDENCE
THAT GREATER SOCIAL ACTIVITY
DIVERSITY, MAY REDUCE
LONELINESS. HIGHER SCORES OF
SOCIAL ACTIVITY DIVERSITY
REPRESENTED GREATER VARIETY,
WITH GREATER REGULARITY IN
SOCIAL ACTIVITY ENGAGEMENT.
THIS STUDY SHOWS THE
POSSIBILITY THAT SOCIAL
ACTIVITY DIVERSITY MAY HELP
ALLEVIATE CHRONIC PHYSICAL PAIN
BY LOWERING FEELINGS OF
LONELINESS.
OUR FUTURE ANALYSIS WILL
EXAMINE POTENTIAL MECHANISMS BY
SOCIAL ACTIVITY DIVERSITY, AS
WELL AS BY INFLAMMATION IN THE
RELATIONSHIP BETWEEN SLEEP
HEALTH PROFILES AND CHRONIC
PAIN. NEXT SLIDE PLEASE.
SO TO SUMMARIZE, SLEEP IS
CLOSELY RELATED TO PAIN IN
DIVERSE AGE-RELATED HEALTH
OUTCOMES. (INDISCERNIBLE) IS
OFTEN FORGOTTEN IN LITERATURE
BUT CHANGES IN RELATIONSHIP TO
HEALTH MAY BEGIN FROM MIDDLE
ADULTHOOD, AND TO IDENTIFY
EARLY RISK FACTORS.
I SHOWED YOU SEVERAL EXAMPLE
STUDIES THAT USED MULTIPLE
SLEEP PARAMETERS. IMPORTANTLY,
MULTIDIMENSIONAL SLEEP HEALTH
APPROACH CAN MORE
COMPREHENSIVELY CAPTURE THE
EFFECTS OF SLEEP ON HEALTH.
LAST, BUT NOT LEAST, SOCIAL
ACTIVITY DIVERSITY AND
INFLAMMATION MAY BE POTENTIAL
MECHANISMS TO REDUCE POOR SLEEP
HEALTH AND CHRONIC PAIN. NEXT
SLIDE PLEASE.
THIS IS MY LAST SLIDE. I WOULD
LIKE TO THANK MY COLLABORATORS
FOR THIS SPAI R01, AS WELL AS
MY RESEARCH ASSISTANT IN MY
RESEARCH LAB FOR THIS HELP.
THANK YOU.
>> THANK YOU VERY MUCH DR. LEE.
THIS IS EXCITING DATA, PEER
INTERESTED IN BIDIRECTIONAL
RELATIONSHIP BETWEEN SLEEP AND
PAIN. ESPECIALLY FOR THE
POTENTIAL TO MODULATE SLEEPER
BETTER PAIN OUTCOMES.
WITH THIS, I AM INVITING OUR
FINAL SPEAKER FOR THE SESSION,
DR. DINA KATABI, PROFESSOR OF
ELECTRICAL ENGINEERING AND
COMPUTER SCIENCE FROM THE
MASSACHUSETTS INSTITUTE OF
TECHNOLOGY IN SHE IS A
COMMUNICATIONS RESEARCHER
WORKING AT THE INTERSECTION OF
... THE TOP OF HER TALK IS AI
FOR MEASURING BRAIN DISEASES
AND THERAPIES DURING SLEEP.
>> I NEED TO SEE MY SLIDES. I
HAVE MANY ANIMATIONS, AS WE
AGREED EARLIER. CURRENTLY THE
HOST SHARING IS DISABLED. CAN
SOMEONE ENABLE IT PLEASE?
>> HELLO DR. KATABI. I WILL BE
SHOWING THE SLIDES FOR YOU.
>> EXCUSE ME? I REALLY NEED,
BECAUSE OF THE ANIMATION AND WE
AGREED I WOULD ADVANCE MY
SLIDES MYSELF. CAN YOU PLEASE
ALLOW ME TO SHARE THE SCREEN?
>> SORRY DR. KATABAI. WE HAVE
INHIBITED ALL THE ANIMATIONS,
DUE TO THE BANDWIDTH WE DON'T
WANT THOSE TO CAUSE ANYTHING.
SO YOU JUST HAVE TO SAY NEXT.
ALL THE ANIMATIONS ARE THERE.
I ASSURE YOU THAT EVERYTHING
PRESENTED WILL BE SHARED.
>> OKAY. LET'S TRY. VERY
INCONVENIENT TO BE HONEST WITH
YOU, AND DIFFERENT FROM WHAT WE
AGREED, BUT LET'S TRY.
THANK YOU VERY MUCH FOR BEING
HERE TO LISTEN TO ME. MY WORK
IS, I AM A COMPUTER SCIENTIST,
INTERESTED IN HOW AI CAN BE
USED TO MEASURING BRAIN
DISEASES AND THERAPIES. IN
PARTICULAR HOW TO DO THOSE
MEASUREMENTS DURING SLEEP.
ONE QUESTION IS, WIDE TO USE AI
TO MEASURE THE BRAIN? THE REAL
ANSWER IS, WE DO NOT HAVE VERY
GOOD WAYS OF MEASURING THE
BRAIN TODAY. NOT LIKE WE HAVE,
LIKE A TEST THAT WOULD ALLOW US
TO HAVE THE MEASUREMENT OF THE
BRAIN. SO IF AI CAN HELP US
ADDRESS ISSUES OF HOW DO WE
UNDERSTAND THE BRAIN AND ITS
DISEASEASES THAT COULD ADDRESS
UNSTEADY DISEASES LIKE MOOD
DISORDER, ETC. NEXT.
THE NATURAL QUESTION IS, HOW
CAN WE USE AI TO UNDERSTAND THE
BRAIN? PARTICULARLY OF COURSE
WE WANT TO UNDERSTAND IT IN
HUMANS, BECAUSE WE KNOW THAT
PARTICULARLY IN NEUROLOGICAL
DISEASES, THE TRANSLATION FROM
ANIMAL MODELS TO HUMANS IS NOT
DONE VERY WELL. IF WE ARE GOING
TO GET INFORMATION AND MEASURE
THE BRAIN IN HUMANS, THEN OF
COURSE WE WANT TO DO IT
CONTINUOUSLY WITHOUT PUTTING
ELECTRODES INTO PEOPLE'S HEADS.
SO THE NATURAL QUESTION IS, HOW
CAN WE DO THAT? NEXT SLIDE
PLEASE.
SO, WHAT WE HAVE DEVELOPED IN
MY LAB AT MIT IS A DEVICE THAT
LOOKS LIKE YOUR WI-FI BOX.
HOWEVER IT IS MUCH SMARTER
WI-FI BOX. IT ANALYZES THE
RADIO SIGNAL THAT BOUNCES
AROUND. AND USING THOSE
SIGNALS, WE ARE ABLE TO EXTRACT
PHYSIOLOGICAL SIGNALS.
PARTICULARLY ABLE TO EXTRACT
SLEEP. AT THE VERY DETAILED
LEVEL. IT CAN ALSO EXTRACT
INFORMATION ABOUT RESPIRATORY
SIGNALS, NOCTURNAL BREATHING,
APNEA, AND CAN EXTRACT
INFORMATION ABOUT MOVEMENT
DISORDERS. AND MULTI-SYMPTOMS
AND INTERACTIONS WITH
CAREGIVERS.
REASON WHAT WE CAN DO THIS
USING RADIO SIGNALS WITHOUT
ASKING PEOPLE TO WEAR WEARABLE
EQUIPMENT UNDER BODIES OR ANY
PIOS, IS BECAUSE WE ARE USING
THE POWER OF AI FOR ANALYZING
THESE AUTONOMIC WAYS. NEXT.
PARTICULARLY AS I SAID, WE WANT
TO USE THE POWER OF SLEEP. ON
THE ONE HAND WE WANT TO BE ABLE
TO UNDERSTAND SLEEP, USING
THESE RADIO SIGNALS WITHOUT
ASKING PEOPLE TO DO A FULL
STUDY. THE OTHER THING IS THAT
WE WANT TO BE ABLE TO LEVERAGE
SLEEP FOR UNDERSTANDING OTHER
DISEASES. NEXT.
SO, WE KNOW THAT WHEN WE GO TO
SLEEP OUR BRAIN WAVES ENTER
DIFFERENT STAGES. AWAKE. LIGHT
SLEEP. DEEP SLEEP AND REM,
RAPID EYE MOVEMENT. AND THE
SLEEP STAGES OF COURSE VERY
MUCH IMPORTANT FOR VARIOUS
SLEEP DISORDERS. BUT AS I SAID
EARLIER, WE ARE ALSO INTERESTED
TO UNDERSTAND HOW THESE SLEEP
STAGES INTERACT WITH DIFFERENT
DISEASES.
FOR EXAMPLE IT IS WIDELY KNOWN
THAT IN ALZHEIMER'S, SLOW WAVE
DURING DEEP SLEEP GETS
IMPACTED. IN DEPRESSION, FOR
EXAMPLE, WE TEND TO SEE RAPID
EYE MOVEMENTS OR REM HAPPENING
EARLY IN THE SLEEP. NEXT SLIDE
PLEASE.
SO, TODAY IF YOU WANT TO GET
SLEEP STAGING VERY ACCURATELY,
YOU HAVE TO SEND SOMEONE TO A
SLEEP LAB AND PUT ELECTRODES ON
THE HEAD AND BODY AND ASKED
THEM TO SLEEP LIKE THIS. THIS
IS THE STANDARD CLINICAL WAY OF
MEASURING SLEEP. SOMETHING
CALLED PSG. AND I'M SURE ALL
OF YOU GUYS ARE AWARE OF IT.
THE PROBLEM WITH PSG, IS THAT
THE PERSON IS SLEEPING WITH ALL
OF THESE ELECTRODES ON THEIR
HEAD AND BODY. THAT ITSELF IS
INTERACTING WITH OUR ABILITY TO
MEASURE NORMAL SLEEP.
THE SECOND THING IS THAT IT
HAPPENS IN THE CLINIC. SO YOU
CANNOT GET THIS SLEEP STUDY
EVERY SINGLE NIGHT. SO ONE OF
THE VERY IMPORTANT THINGS THAT
WE AIM TO DO, AND WE DID
SUCCESSFULLY, IS TO BE ABLE TO
EXTRACT INFORMATION ABOUT SLEEP
STAGING FROM WIRELESS SIGNALS.
NEXT SLIDE PLEASE.
HERE YOU SEE HOW OUR DEVICE
TRANSMITS SIGNALS AND ANALYZES
THE REFLECTION USING AI. AND
THAT IS BECAUSE OF THE
HIPNOGROUND, USED TO ANALYZE
THEIR SLEEP. IT TELLS YOU FOR
EVERY 30 SECONDS OF SLEEP,
WHETHER THE PERSON IS IN LIGHT
SLEEP, DEEP SLEEP OR REM.
TODAY WE ARE ABLE, OR THE
TYPICAL WAY OF DOING A SLEEP
STUDY IS TO SEND SOMEONE TO A
SLEEP LAB, AND PUTTING THE
ELECTRODES THAT I SHOWED YOU ON
THE PREVIOUS SLIDE ON SOMEONE'S
HEAD AND BODY. BUT EVEN WHEN
WE DO SLEEP STUDIES IN THE
HOME, WITH TYPICALLY DO NOT GET
THE HIPNOGRAM. FOR HOMESTUDY
WE USE A BREATHING BELT TO
MEASURE APNEA BUT NOT GET THE
HIPNOGRAM. HERE WE CAN GET THE
HIPNOGRAM AT HOME EVERY NIGHT
AND COMPARED TO THE PSG, AND
THEN FROM THE LAB WE CAN SHOW
THE ACCURACY IS COMPARABLE TO
TWO SLEEP TECHNICIANS, AND THE
CONSISTENCY BETWEEN THEM AS
THEY LOOK AT THE DATA IN A
MANUALLY SCREEN FOR SLEEP
STAGES. NEXT SLIDE PLEASE.
OF COURSE NOT ONLY WE'RE ABLE TO
SEE SLEEP STAGES, WE CAN ALSO
GET RESPIRATORY SIGNAL, HERE YOU
SEE SOMEONE, INHALE, EXHALE, HE
EXHALES, DOES NOT INHALE, WE CAN
GET HYPNOGRAM, RESPIRATORY
SIGNAL, COMPARING TO THE GOLD
STANDARD, IN THE CASE OF
BREATHING, SIMILAR TO THE
BREATHING BELT YOU USE IN THE
FDA APPROVED BREATHING BELT IN
SLEEP STUDIES IN THE SLEEP LAB.
NEXT.
SO, I'M GOING TO SHOW YOU A FEW
WAYS THAT WE USE THIS SLEEP
DATA, NOCTURNAL BREATHING, AND
ONE OF THEM IS HOW WE USE IT TO
ANALYZE AND DETECT PARKINSON'S
DISEASE.
SO PARKINSON'S IS THE FASTEST
GROWING NEUROLOGICAL DISEASE IN
THE WORLD, SECOND MOST COMMON
NEUROLOGICAL DISEASE AFTER
ALZHEIMER'S.
ONE OF THE BIG PROBLEMS WITH
PARKINSON'S SIMILAR TO
ALZHEIMER'S, WE DETECT THE
DISEASE LATE, TYPICALLY LIKE TEN
YEARS AFTER ONSET OF THE
DISEASE.
AND THE REASON FOR THAT IS THAT
WHEN WE DETECT IT, WE'RE
DETECTING MOTOR SYMPTOMS BUT
THEY HAPPEN LATER IN DISEASE.
BY THE TIME THE MOTOR SYMPTOMS
HAPPEN AND WE DIAGNOSE
PARKINSON'S, BETWEEN 50% TO 80%
OF THE DOPAMINERGIC NEURONS IN
THE BRAIN, THE NEURONS RELATED
TO DOPAMINE, IMPORTANT FOR THE
CASE OF PARKINSON'S, IS ALREADY
IMPAIRED.
THAT THE VERY IMPORTANT IF WE
WANT TO MAKE ADVANCEMENT IN
PARKINSON'S, TO DETECT THE
DISEASE EARLY.
WHAT WE DID, WE TRIED TO USE
NOCTURNAL BREATHING, TO USE
SOMETHING OTHER THAN MOTOR
SYMPTOMS, SOMETHING THAT HAPPENS
EARLY IN THE DISEASE, WHICH IS
SO BREATHING CHANGES EARLY IN
THE DISEASE, SO THAT WE TRY TO
SEE WHETHER WE CAN USE BREATHING
AS A WAY TO DETECT PARKINSON'S.
NEXT.
SO, WE ASKED A VERY SIMPLE
QUESTION.
CAN A.I. DETECT PARKINSON'S
DISEASE FROM NOCTURNAL
BREATHING.
WE TAKE THE RESPIRATORY SIGNAL
DURING SLEEP, NOCTURNAL
BREATHING.
NEXT.
TO ANALYZE ONE SAMPLE, ONE
NIGHT, NOCTURNAL BREATHING.
NEXT.
AND NEURAL NETWORK TRIES TO
ANSWER TWO QUESTIONS.
DOES THIS PERSON FROM
PARKINSON'S, IF THEY DO WHAT IS
THE SEVERITY OF THESE DISEASE
ACCORDING TO THE GOLD STANDARD?
NEXT.
THIS IS A LARGE STUDY THAT
INVOLVES OVER 7,000 INDIVIDUALS,
700 WITH PARKINSON'S, AND WE DID
THIS IN COLLABORATION WITH MASS
GENERAL, MAYO CLINIC, UNIVERSITY
OF ROCHESTER MEDICAL SCHOOL, AND
THE RESULTS APPEARED IN "NATURE
MEDICINE."
NEXT.
SO, HERE THE RESULTS FROM THE
STUDY, YOU SEE ON THE X-AXIS THE
SENSITIVITY, ON THE Y-AXIS --
SORRY, ON X-AXIS SPECIFICITY, ON
Y-AXIS SENSITIVITY.
THESE ARE STANDARD WAYS OF
ASSESSING MACHINE LEARNING AND
MEDICINE, THE IMPORTANT NUMBER
IS THIS AUC, WHICH YOU CAN
INTERPRET AS ACCURACY, 90% IN
THIS CASE.
SO WHAT THIS GRAPH IS TELLING US
IS THAT WE CAN DETECT WHETHER
SOMEBODY HAS PARKINSON'S JUST BY
ANALYZING THEIR BREATHING DURING
SLEEP AND EVEN WE CAN GET THE
BREATHING FROM A SIGNAL, DON'T
HAVE TO WEAR THEM WEAR A
BREATHING BELT.
THIS IS QUITE POWERFUL.
IT'S A FIRST TIME ANYONE HAS
SHOWN YOU CAN DETECT PARKINSON'S
FROM SOMEONE'S NOCTURNAL
BREATHING.
NEXT.
WE CAN DETECT PARKINSON'S FROM
NOCTURNAL BREATHING BUT ANOTHER
INTERESTING QUESTION CAN WE
DETECT IT EARLIER BEFORE
DIAGNOSIS.
SO, OF COURSE, THIS IS MUCH
HARDER STUDY BECAUSE WE NEED TO
MONITOR PEOPLE BEFORE THEY WERE
DIAGNOSED FOR PARKINSON'S, LIKE
A CHECKEN AND EGG PROBLEM, YOU
DON'T KNOW YET WHO IS GOING TO
BE DIAGNOSED LATER.
WE FOUND THE STUDY, A SLEEP
STUDY, WHICH HAS TWO VISITS TO
THE SLEEP LAB AND IT HAS THE
BREATHING FROM THOSE TWO VISITS.
PEOPLE GO TO TWO VISITS AND
THEIR BREATHING SIGNAL IS
COLLECTED.
THE FIRST VISIT IS -- SECOND IS
SEPARATED BY SIX YEARS FROM THE
FIRST VISIT.
SO, WE ANALYZED WHETHER WE CAN
USE THE FIRST VISIT AND WE SEE
THAT PEOPLE WHO GOT SIX YEARS
LATER, THEY DECLARED THAT THEY
HAVE PARKINSON DIAGNOSIS.
NEXT.
SO, HERE YOU CAN SEE LIKE THIS
STUDY HAS ABOUT 500 INDIVIDUALS.
THE GUYS DURING THE FIRST VISIT
HAVE PARKINSON'S, AND EVEN SIX
YEARS LATER, THEY STILL DON'T
HAVE PARKINSON'S.
NOW, THE DATA IN BLUE ARE FOR
PEOPLE IN THE FIRST VISIT DIDN'T
HAVE PARKINSON'S, BUT SIX YEARS
LATER THEY CAME, AS I SAID, OH,
I WAS DIAGNOSED WITH
PARKINSON'S.
BUT WE ARE ANALYZING THEIR
BREATHING, NOCTURNAL BREATHING
DURING SLEEP FROM THE FIRST
VISIT.
AS YOU CAN CLEARLY SEE, THE
ALGORITHM ALREADY IS SAYING
THESE GUYS ARE DIFFERENT, 75% OF
THEM, A.I. ALGORITHM, DIAGNOSED
AS HAVING PARKINSON'S ALREADY.
SIX YEARS EARLIER THAN WHEN THEY
TOLD US THEY HAD PARKINSON'S.
NEXT.
SO, I TOLD YOU ABOUT HOW WE
CAN -- A.I. CAN EXTRACT
INFORMATION ABOUT PARKINSON'S, A
NEURAL DEGENERATIVE DISEASE.
I WANT TO TELL YOU HOW IT CAN
EXACT INFORMATION FROM SLEEP AND
BREATHING DURING SLEEP ABOUT
DEPRESSION AND ANTIDEPRESSANTS.
WE ASKED PEOPLE LIKE WHEN WE
GIVE ANTI-DEPRESSANT, IS IT
WORKING FOR YOU, DO YOU FEEL
BETTER.
WE DON'T HAVE ANY CONCRETE
MEASUREMENT AND IT IS WIDELY
KNOWN THERE IS A HUGE PLACEBO
RESPONSE, SO ONE OF THE THINGS
THAT WE TRIED TO UNDERSTAND IS
CAN MACHINE LEARNING BE ABLE TO
USE SLEEP AND BREATHING DURING
SLEEP TO UNDERSTAND ARE THEY
DEPRESSED.
LIKE IN PARKINSON'S, WE TAKE THE
WIRELESS SIGNAL, TRANSFORM IT
INTO THE RESPIRATORY SIGNAL, AND
THEN WE TRAIN A NEURAL NETWORK
THAT TAKES NOCTURNAL BREATHING.
SO DOES THIS PERSON TAKE
ANTI-DEPRESSANT OR NOT?
THE OBJECTIVE IS NOT KNOW THAT,
THAT YOU CAN ASK THE PERSON.
BUT IF THE NEURAL NETWORK FROM
NOCTURNAL BREATHING DURING SLEEP
BE ABLE TO DETECT IMPACT OF
ANTI-DEPRESSANT THAT THE PERSON
IS TAKING ANTI-DEPRESSANT IT
MEANS IT IS DISCOVERING A CHANGE
IN THE PHYSIOLOGY OF THE PERSON
ASSOCIATED WITH SLEEP AND IMPACT
OF ANTI-DEPRESSANT ON SLEEP.
THIS STUDY HAS 2,000
INDIVIDUALS, 200 TAKING
ANTI-DEPRESSANT.
YOU SEE THE RESULT.
AGAIN, THESE ARE SIMILAR TO THE
CURVE YOU SAW EARLIER,
SENSITIVITY AND SPECIFICITY ON
THE TWO AXES, BUT THE LONGER YOU
SEE, YOU CAN INTERPRET THIS AS
ACCURACY AS WE SAID EARLIER, SO
IT TELLS YOU THAT FROM ONE NIGHT
OF NOCTURNAL BREATHING DURING
SLEEP, THE NEURAL NETWORK CAN
TELL WHETHER THIS PERSON HAS --
IS TAKING ANTIDEPRESSANTS WITH
ACCURACY 86%.
NEXT.
HERE I SHOW YOU AN EXAMPLE FROM
ONE PERSON WHO WAS MOVED, ON
ANTI-DEPRESSANT, PUT ON PROZAC
IN THE MIDDLE OF THE STUDY.
ON THE X-AXIS TIME, ON THE
Y-AXIS YOU SEE THE OUTPUT OF THE
MACHINE LEARNING.
AND THE ORANGE LINE IS JUST THIS
OUTPUT OF THE MACHINE LEARNING.
YOU CAN SEE THAT AT THE
BEGINNING LIKE THIS PERSON IN
THE MIDDLE OF THIS STUDY WAS PUT
LIKE AT THE TIME WHEN YOU SEE
THE ORANGE LINE GOING HIGH THE
PERSON WAS PUT ON
ANTI-DEPRESSANT, STARTED PROZAC.
BEFORE THAT POINT THE ORANGE
LINE IS LOW, MACHINE LEARNING OR
A.I. IS SAYING, NO, THIS PERSON
IS NOT TAKING ANTI-DEPRESSANT.
THEN YOU CAN SEE HOW THE ORANGE
LINE JUMPS, CROSSING THE
THRESHOLD OF .5, 50%, WHICH SAYS
THIS PERSON STARTED TAKING
ANTI-DEPRESSANT AND YOU CAN SEE
IT RAMPS UP SLOWLY, VERY
INTERESTING BECAUSE WE KNOW THAT
THE IMPACT OF ANTI-DEPRESSANT IS
NOT IMMEDIATE, BUILDS EVERY OVER
TIME.
A I. A.I. IS DETECTING CHANGES
IN SLEEP AND NOCTURNAL BREATHING
IN THIS INDIVIDUAL.
NEXT.
I WANT TO END WITH TALKING ABOUT
GENERATIVE A.I.
AND FOR BRAIN DATA.
AGAIN, IN THE CONTEXT OF SLEEP.
SO OF COURSE WE ALL KNOW ABOUT
CHATGPT, YOU TRIED YOU THE, AT
LEAST THE VAST MAJORITY TRIED
ChatGPT.
NEXT.
SO, IN TODAY GENERATIVE MODEL
FROM TEXT LIKE IN ChatGPT OR
WE GENERATE IMAGES FROM TEXT.
HERE YOU SEE AN EXAMPLE WHERE
YOU PROMPT THE GENERATIVE MODEL
WITH TEXT, GENERATE FOR ME A
PICTURE, PHOTO OF A CORGI
PLAYING A TRUMPET, IT GENERATES
WHAT YOU SAY AT THE PROMPT.
SO CAN WE USE GENERATIVE MODELS
SIMILARLY TO GENERATE A
MEASUREMENT AS THE PERSON'S
BRAIN DATA, THE EEG.
NEXT.
SO HERE YOU SEE WHAT WE ARE
LOOKING INTO, WE'RE LOOKING TO
TAKE RADIO SIGNAL, FROM RADIO
SIGNAL WE WANT TO BE ABLE -- OF
COURSE WE KNOW FROM RADIO
SIGNALS WE CAN EXTRACT
BREATHING, NOCTURNAL BREATHING
DURING SLEEP, AS I SAID, WHICH
IS VERY POWERFUL.
AND FROM THAT, WE WANT TO BE
ABLE TO GENERATE SOMEONE'S EEG.
NEXT.
SO INITIAL RESULTS THAT SHOWS
ACTUALLY THIS CAN WORK.
WE CAN GENERATE THE EEG DURING
SLEEP IN AN INDIVIDUAL JUST BY
LOOKING AT RESPIRATORY SIGNAL
AND THAT RESPIRATORY SIGNAL WE
CAN EVEN GET FROM WIRELESS
SIGNALS WITHOUT TOUCHING THEIR
BODY.
NEXT.
THIS IS THE EEG, GROUND TRUTH
EEG DURING SLEEP, ONE NIGHT OF
SLEEP, FOR A PATIENT OF
ALZHEIMER'S, 83 OLD.
AND YOU CAN SEE THIS IS ONE
NIGHT, LIKE THE PERSON ENTERED
LIKE DIFFERENT SLEEP STAGES AND
THIS IS WHAT'S CALLED EEG
SPECTRO GRAM.
NOW YOU SEE THE GENERATED EEG.
AGAIN, THIS IS GENERATED WITH
THE MODEL BY TAKING WIRELESS
SIGNAL, TRANSFORMING BREATHING
DURING SLEEP, WE TAKE THE
BREATHING DURING SLEEP AND WE
GENERATE THE SIGNAL, THE EEG
SPECTROGRAM, YOU CAN SEE IT'S
PRETTY ACCURATE.
REALLY, SLEEP IS VERY POWERFUL.
A.I. CAN ALLOW US TO UNDERSTAND
SLEEP MUCH, MUCH MORE
INTERESTING AND POWERFUL WAY TO
UNDERSTAND OUR DISEASES AND OUR
BRAIN SIGNAL.
NEXT.
SO, THIS GETS ME TO THE END OF
MY PRESENTATION WHERE I SHOWED
YOU THAT YOU NEED WIRELESS
SIGNAL IN THE ENVIRONMENT WE CAN
DO EVERY NIGHT SLEEP STUDY THAT
GENERATES HYPNOGRAM, GENERATES
NOCTURNAL BREATHING SIGNAL ON
THE PERSON'S OWN BEDROOM AND
USING THAT INFORMATION CAN START
UNDERSTANDING AND STUDYING
DIFFERENT NEUROLOGICAL DISEASES
AND THE IMPACT OF EVEN
MEDICATION AND THERAPY ON THESE
NEUROLOGICAL DISEASES.
THANK YOU VERY MUCH.
>> THANK YOU VERY MUCH, DR.
KATABI, THIS IS FASCINATING
DATA.
THANK YOU TO ALL THE SPEAKERS.
WE HAVE ABOUT THREE MINUTES
BEFORE THE NEXT SESSION.
I KNOW THAT WE'VE GOT SEVERAL
QUESTIONS DURING THIS SESSION
NUMBER THREE FROM THE VIDEOCAST
AUDIENCE.
I WILL ASK FOR SOME QUESTIONS.
THANK YOU ALL VERY MUCH FOR
WATCHING THIS SESSION.
THANK YOU.
>> THANKS.
FASCINATING PRESENTATION,
PARTICULARLY DR. KATABI RAISING
ONE OF THE POINTS I WANTED TO
MAKE CLEAR DURING THIS WORKSHOP,
THERE'S A LOT OF SIGNALS THAT
ARE DETECTABLE DURING SLEEP THAT
REFLECT DISEASE, REFLECT HEALTH
STATES OF THE PERSON SLEEPING.
WE NEED SOMETHING LIKE A.I. TO
BE ABLE TO DETECT AND ANALYZE
SIGNALS ACCURATELY.
I HAVE A COUPLE QUESTIONS.
YOU MAY HAVE MENTIONED THIS,
STUDIES LOOKING WHETHER CPAP
EFFICACY VARIES IN SUBTYPES, IS
IT MORE EFFECTIVE, HAVE PEOPLE
LOOKED AT THAT?
>> YEAH, THERE'S LIKE A SINGLE
STUDY BUT NOT UNDER LIKE A TRIAL
FRAMEWORK SO IT WAS MORE LIKE AN
OBSERVATIONAL SETTING.
THE ORIGINAL COHORT FROM ICELAND
WAS FOLLOWED FOR TWO YEARS, SOME
WEARING CPAPs, SOME NOT, THEY
DID SEE EFFECTS ON LIKE SYMPTOM
PRESENTATION TO BE STRONGER
AMONG THOSE THAT HAVE MORE
SYMPTOMS.
NOT LIKE ANY OTHER MARKER, SUCH
AS INTERMEDIATE CARDIOVASCULAR
ENDPOINT IN THAT STUDY, SO
THAT'S LIKE AN AREA OF FUTURE
INVESTIGATION.
>> SURE.
WHILE I HAVE YOUR ATTENTION,
THIS IS MORE OF A GENERAL
COMMENT FROM ROBERT THOMAS, YOU
MAY KNOW AT HARVARD, HAS A
GENERAL COMMENT ABOUT HYPOXIC
BURDEN.
IN HIS OPINION CARES MORE ABOUT
SLEEP QUALITY AND SLEEPINESS AND
FATIGUE, NOT SEEKING THERAPY FOR
PREVENTION, NO NEED NOR HYPOXIA,
FOR THEM TO FEEL MISERABLE IN
ONE TREATMENT.
IN EPIDEMIOLOGICAL STUDIES USING
SCORING, DIMENSIONS ARE NOT
PROPERLY CAPTURED.
AT THE INDIVIDUAL LEVEL WE CAN
USE THE BURDEN EHI AS A
MOTIVATIONAL FACTOR, LOW BURDEN
IN ITSELF SHOULD NOT BE
CONSIDERED MILD APNEA, BASED ON
AHI, BECAUSE AS WE'RE SAYING, WE
REALLY NEED TO LISTEN TO THE
PATIENTS AND WHAT THEY ARE
EXPERIENCING.
>> ABSOLUTELY.
I THINK THAT'S WHY ADDING
DIMENSION OF PATIENT-REPORTED
OUTCOMES, MORE IMPORTANTLY
BRINGING BACK MY LAST TOPIC, MY
TAKEAWAY MESSAGES, ABOUT
VIABILITY.
WE DON'T REALLY KNOW A LOT ABOUT
WHAT HAPPENS, IF SOMEONE HAS
CHANGES IN SEVERITY OF DISEASE
HOWEVER WE'RE DEFINING, AHI,
HYPOXIC BURDEN, NOW THERE ARE
STUDIES, PARTICULARLY FROM THE
GROUP IN AUSTRALIA USING OTHER
METRICS WITHIN LIKE SENSORS
DEMONSTRATING CORRELATIONS
BETWEEN DIFFERENCES LIKE CHANGES
IN SLEEP, DURATION AND CHANGES
IN AHI, WITH TRACKING WITH
CHANGES IN BLOOD PRESSURE AND I
THINK MY HYPOTHESIS IS THAT
WE'RE MISSING THIS COMPONENT OF
HETEROGENEITY THAT'S RIGHT THERE
THAT WE JUST DON'T HAVE LIKE THE
FACTOLOGY YET TO MEASURE AT
LARGE SCALE BUT GIVING
RESULTING, MAYBE WE CAN PREDICT
SEVERITY METRICS FROM THOSE
WIRELESS SENSORS WOULD BE VERY
INTERESTING TO START DOING THOSE
MEASUREMENTS FROM A LONG TERM,
YEAH.
>> THE INFORMATION IS THERE.
WE JUST HAVE TO DETECT AND
ANALYZE IT.
A COUPLE THINGS FOR DR. LEE, ONE
THING HAS SLEEP EXTENSION
PROTOCOLS BEEN TRIED FOR CHRONIC
PAIN, THAT YOU KNOW OF?
SOOMI, YOU'RE ON.
YOU'RE MUTED.
>> MOST INTERVENTION STUDIES, AS
FAR AS I KNOW, USED CBT-I OR CBT
PROTOCOL, SLEEP EXTENSION, WHEN
WE THINK ABOUT CHRONIC PAIN AS
THE OUTCOME, THE POPULATION OF
INTEREST IS OLDER ADULTS, AND
OLDER ADULTS, SHORTER DURATION,
THEY HAVE A DIFFERENT SLEEP
ISSUE.
SO I THINK, YEAH, IN TERMS OF
EFFECT OF SLEEP EXTENSION, OF
PAIN, WE DON'T KNOW MUCH ABOUT
IT AND THERE'S LACK OF EVIDENCE,
I THINK.
>> I
>> A COMMENT FROM ROBERT THOMAS,
YOU SAY FROM DAN BICE, A
SUBJECTIVE MEASURE, WHY NOT
INTEGRATE CHANGES IN BREATHING,
AUTONOMIC FUNCTION, OBJECTIVE
SLEEP QUALITY SO WE KNOW --
THAT'S THE HOLY GRAIL, RIGHT?
HOW DO WE KNOW WHEN PEOPLE ARE
SATISFIED WITH SLEEP EXPERIENCE
AND I DON'T KNOW IF ANYBODY HAS
COME UP WITH A GOOD INDEX OF
THAT YET BUT THAT'S WHAT HE'S
ASKING ABOUT.
IF THERE'S A GOOD OBJECTIVE
MEASURE OF IT.
>> RIGHT.
THE SLEEP HEALTH DIMENSIONS, CAN
BE MEASURED BY DIFFERENT
MODALITIES, INCORPORATING SOFT
REPORT ACTIGRAPHY.
IT IS BEST UNDERSTAND BY
SELF-REPORT BECAUSE WE'RE
CONCERNED ABOUT PERCEIVED
SATISFACTION.
BUT OTHER DIMENSIONS CAN BE
MEASURED TO SELF-REPORT.
>> THE HOLY GRAIL IS THE
BIOMARKER FOR SLEEP, RIGHT?
REFLECTS SLEEP OR SLEEP
DISTURBANCES.
AND THERE ARE MANY PEOPLE
WORKING ON THAT.
I DON'T THINK THERE'S ANY BIG
BREAKTHROUGHS YET.
DR. KATABI IS STILL ON.
WI-FI ANALYSIS IS INTRIGUING.
CAN IT DETECT CHANGES IN GAIT OF
INDIVIDUALS ASSOCIATED WITH DAY
TIME SLEEPINESS AND RISK OF
FALLS?
>> YEAH, SO, WE CAN TRACK THE
GAIT.
IN FACT WE HAVE EVEN NEWER
RESULTS THAT SHOW NOT JUST THAT,
WE CAN TRACK FULL LIKE STICK
ANYTHING, LIKE THE SKELETON OF
THE PERSON AS THEY ARE MOVING.
WE CAN ALSO DETECT FALLS.
WE HAVE PEOPLE WHERE WE SHOW WE
CAN DETECT THE FALL.
WE HAVEN'T LOOKED AT THE ISSUE
CAN YOU PREDICT SUSCEPTIBILITY
OR RISK TO FALLING BUT THE
LITERATURE SHOWS CHANGES OF GAIT
ARE PREDICTOR FOR LATER -- FULL
REST, I WOULD SAY THE ANSWER AT
THE HIGH LEVEL IS YES BUT WE
HAVEN'T PARTICULARLY LOOKED AT
THAT PARTICULAR PROBLEM.
>> GREAT.
THANKS, AMAZING WHAT YOU CAN
DETECT WITH ABILITIES YOU HAVE.
A COUPLE MORE QUESTIONS, I'M
PUSHING TIME HERE.
THIS IS A Q&A SESSION FOR THIS
GROUP OF TALKS.
IS IT POSSIBLE TO -- IT'S
DIFFICULT TO COMPARE, UTILIZE
DIFFERENT SETS BETWEEN
MODALITIES, YOU WOULD
ACKNOWLEDGE THAT.
DO YOU HAVE ANY COMMENTS ON
THAT?
>> YEAH, I DO HAVE A COMMENT.
I AGREE.
I THINK THAT ONE OF THE REASONS
WHY SOME RESULTS ARE NOT FULLY
APPLICABLE BECAUSE PEOPLE ARE
MEASURING SIMILAR THINGS BUT NOT
EXACTLY THE SAME WAY.
I THINK THAT JUST BRINGS WHAT
WAS DISCUSSED LAST TIME,
YESTERDAY, ABOUT
STANDARDIZATION, THERE'S JUST SO
MUCH WE CAN DO WHEN TRYING TO
HARMONIZE DATA, BUT I KNOW THERE
ARE SOME -- MANY INCREDIBLE
EFFORTS FROM THE NSRR GROUP
WORKING TOWARDS HARMONIZING BOTH
CONCEPTUALLY AND TECHNICALLY
ELEMENTS ABOUT SLEEP BEING THAT
SELF-REPORT, BEING THAT SOME
OBJECTIVE METRICS THAT WILL HELP
US MEASURE THIS AND PERHAPS
UNDERSTAND WHETHER THOSE
DIFFERENCES CAN EXPLAIN, REALLY
DETECT OR EXPLAIN DIFFERENCES
SEEN ACROSS STUDIES.
I THINK THAT COULD EXPLAIN PART
OF THE PROBLEM BUT I THINK THERE
ARE OTHER APPROACHES THINKING
ABOUT PERSPECTIVE STUDIES AND
MEASURING THIS.
THERE'S A LOT OF TALK OF USING
STANDARDIZED MEASURES SUCH AS
PROMIS FOR CAPTURING
PATIENT-REPORTED OUTCOMES IN
CONTEXT AND I THINK WE SHOULD
START THINKING ABOUT USING
THOSE, CHARACTERIZING THOSE MORE
SO WE HAVE A REFERENCE.
NOW, WHETHER THOSE ARE BEST
MEASURES ARE NOT IS ANOTHER
QUESTION.
BUT ADDITIONAL STUDIES TO BETTER
CHARACTERIZE THOSE AND PROMOTE
MEASURES THAT ARE SYNERGIZED
ACROSS THE BOARD FOR PROSPECTIVE
STUDIES IS DEFINITELY WARRANTED.
>> ANOTHER QUESTION IN THE
OPPOSITE DIRECTION, IF YOU HAVE
A LARGE EPIDEMIOLOGIC STUDY WITH
MORE DATA MODALITIES RECORDED
HOW ABOUT EXPANDING NUMBER OF
PHENOTYPES THAT YOU'RE LOOKING
AT, WILL IT GIVE YOU LIKE FIND A
RESOLUTION, THE QUESTION
ULTIMATELY HOW DETAILED OR
DEEPLY DO WE NEED TO GO WITH
PHENOTYPING?
>> GREAT QUESTION.
IT DEPENDS ON THE RESOURCES THAT
ARE AVAILABLE TO ACTUALLY DO
THAT, RIGHT?
IF WE CAN DEEP PHENOTYPE, LIKE A
WELL-DEFINED PATIENT OR
POPULATION, THAT'S
REPRESENTATIVE, THAT'S BEING
COLLECTED ACROSS SETTINGS,
THAT'S NOT NECESSARILY
EPIDEMIOLOGICAL STUDIES
HISTORICAL DONE IN CERTAIN
GROUPS WE SHOULD EXTEND THAT, IF
WE CAN DO INVESTMENTS THAT'S A
GREAT PERSPECTIVE, LIKE I DO
BELIEVE THAT TECHNOLOGY IS
IMPROVING, ACCESS TO TECHNOLOGY,
ACCESS TO DIVERSE COHORTS OF
PATIENTS, IMPORTANCE OF PATIENT
STAKEHOLDERS, IN ORDER FOR US
ENROLLED PARTICIPANTS FROM
DIFFERENT GROUPS, I'M EXCITED
FOR THIS SESSION ON ETHICS LATER
ON, THAT HOPEFULLY WILL BRING
SOME OF THAT TO LIGHT AND HOW WE
CAN DRIVE A PICTURE OF THAT
PERSPECTIVE.
>> I'M GOING TO TURN IT OVER TO
MY PROGRAM OFFICER FROM NCI
MODERATING THE NEXT SESSION.
THANKS TO THE SPEAKERS FROM THE
MOST RECENT SESSION, INTRIGUING
RESULTS.
>> THANKS, TODD.
>> WELCOME TO SESSION NUMBER 4,
WE HAVE THREE GREAT SPEAKERS FOR
YOU, DR. KISS, S PIRA, MORIES.
WE'LL START WITH DR. KISS, A
RESEARCH SCIENTIST AT SRI
INTERNATIONAL CENTER FOR HEALTH
SCIENCES, BACKGROUND FORMS THE
FOUNDATION FOR HER RESEARCH AT
THE INTERSECTION OF DATA SCIENCE
AND NEUROSCIENCE.
DR. KISS SPECIALIZES IN
ANALYZING MULTI-MODAL LARGE
SCALE BIOMEDICAL DATASETS TO
INVESTIGATE HOW SLEEP PATTERNS,
BRAIN FUNCTIONS AND SOCIAL
CONNECTEDNESS INTERSECT WITH
HEALTH.
>> THANK YOU VERY MUCH.
>> WE CAN'T HEAR YOU RIGHT NOW.
>> CAN YOU HEAR ME NOW?
>> YES, I CAN HEAR YOU.
>> WONDERFUL.
>> THANK YOU.
NOW YOU'RE ON.
>> THANK YOU VERY MUCH FOR THIS
KIND INTRODUCTION.
I'M HONORED BY THIS OPPORTUNITY
TO TALK ABOUT OUR RECENT
PROJECT.
IN ADDITION TO PRESENTING
SCIENTIFIC RESULTS MY AIM TO
GIVE BROADER PERSPECTIVE ON USE
OF MACHINE MODELS BY
HIGHLIGHTING POTENTIALS AND SOME
CHALLENGES THAT WE'RE FACING.
NEXT SLIDE PLEASE.
THIS IS PART OF A PROJECT
BETWEEN OUR LAB AND UCSF.
LET'S GET STARTED.
OBESITY AND CO-MORBIDITIES
REPRESENT THE MOST SERIOUS RISK
FACING U.S. ADOLESCENTS.
BETWEEN 2017 AND 2020 THE
PERCENTAGE OF OBES ADOLESCENCE
WAS 22%.
EARLY ADOLESCENCE IS HIGH-RISK
FOR ADULT OBESITY BUT ALSO
CARDIOVASCULAR HEALTH PROBLEMS.
SLEEP IS A CRITICAL RISK
FACTOR, ASSOCIATED WITH 80%
INCREASE IN OBESITY AMONG
ADOLESCENTS.
NEXT SLIDE PLEASE.
SOME PROPOSED MECHANISMS
INCLUDED SLEEP-RELATED CHANGES
IN ENERGY INTAKE, DECREASED
PHYSICAL ACTIVITY, NEGATIVE
EMOTIONS.
NEXT SLIDE PLEASE.
WE AIM TO EXPLORE OBESITY CAN BE
PREDICTED USING DATA RECORDED IN
THE ADOLESCENT BRAIN COGNITIVE
STUDY.
OUR GOAL WAS TO IDENTIFY MOST
IMPORTANT MEASURES.
PRIOR STUDIES SHOW SLEEP AND
ACTIVITY MEASURES IMPACT
OBESITY, IT IS IMPORTANT TO NOTE
THAT IN TRADITIONAL APPROACHES
SIGNIFICANT EFFECT DOES NOT
NECESSARILY MEAN A LARGE EFFECT
SIZE OR ACCURATE PREDICTION.
HERE MACHINE LEARNING MODELS
ASSESS ACCURATE PREDICT STATUS
FROM THESE DATA, DIFFERENTIATING
OBESE FROM NON-OBESE.
NEXT SLIDE PLEASE.
ABCD STUDY IS THE LARGEST
INITIATED IN 2018 WITH 11,000
PARTICIPATING, PUBLICLY
AVAILABLE, QUITE BIG.
DURING THE SECOND YEAR VISIT
PARTICIPANTS WERE INVITED TO
WEAR DEVICES FOR THREE WEEKS.
PRESENTING SUBSAMPLE OF COHORT.
WE INCLUDED PARTICIPANTS WITH
BOTH FITBIT AND BMI DATA.
17% OF ADOLESCENTS CLASSIFIED
AS BEING OBESE.
YES?
THIS RATIO WAS 13%.
NEXT SLIDE PLEASE.
WE ALL KNOW WEARABLE INDUSTRY
OFFERS WIDE RANGE OF
COMMERCIALLY AVAILABLE DEVICES
THAT HAVE INCREASED OUR HEALTH
AWARENESS BY MEASURING SLEEP AND
PHYSICAL ACTIVITY.
IN OUR MODEL, WE INCLUDED
MEASURES OF PHYSICAL ACTIVITY
AND CARDIOVASCULAR IN TERMS OF
SLEEP AND RESTING HEART RATE AND
STEP COUNTS.
AND WE INCORPORATES MEASURES
CONCERNING SLEEP TIMING,
QUALITY, REGULARITY.
WHEN I SAY REGULARITY I MEAN DAY
TO DAY VARIABILITY MEASURED
DIFFERENT WAYS.
AND WE ANALYZED DAILY MEASURES.
GRAPH HERE SHOWS PHYSICAL
ACTIVITY LEVELS ACROSS THE DAYS
OF THE WEEK WITH DIFFERENT LINES
REPRESENTING THREE WEEKS OF DATA
COLLECTION.
NOTABLY PHYSICAL ACTIVITY LEVELS
WHY HIGHER DURING THE FIRST WEEK
OF THE DATA COLLECTION.
LEADING US TO FOCUS ON THE
SUBSEQUENT TWO WEEKS FOR MORE
TYPICAL REPRESENTATION OF
ACTIVITY AND SLEEP PATTERNS.
I WANT TO NOTE THAT WE PAY LOTS
OF ATTENTION ON DATA PROCESSING
AND FEATURE SELECTION, AND OUR
MODELS ARE INFORMED IN THE SENSE
THAT THEY ARE CONSTRUCTED FROM
CAREFULLY CHOSEN MEASURES THAT
ALIGN WITH EVIDENCE AND
THEORETICAL CONSIDERATIONS.
MOSTLY AVOIDING REDUNDANT
INFORMATION.
THIS ENSURES OUR MODELS DO MORE
THAN JUST PREDICT.
THEY OFFER MEANFUL INSIGHT
CRUCIAL FOR UNDERSTANDING
NUANCES OF OBESITY IN
ADOLESCENTS.
NEXT SLIDE PLEASE.
WE EVALUATED MODELS, COMMON IN
MACHINE LEARNING, AND FOUND OUR
PRIMARY METHOD, EXPLAINABLE
BOOSTING MACHINES, WHICH IS
BASED ON DECISION TREES,
PERFORMED WELL.
AND AFTER ADDRESSING AND
SELECTING 70 PREDICTORS, WE
CONDUCTED GRID SEARCH, THIS IS A
CRUCIAL STEP TO ENSURE
GENERALIZABILITY OF OUR RESULTS.
THE FIGURE HERE IS ITERATIVE
PROCESS, EBM MODEL FOR
CLASSIFICATION.
STARTING WITH BASIC MODEL, EBM
SEQUENTIALLY UPDATED PREDICTIONS
BY CYCLING THROUGH EACH FEATURE
TO LEARN UNIQUE CONTRIBUTIONS TO
GRADIENT BOOSTING.
IT EXPLORES DIRECTIONS BETWEEN
PAIRS OF FEATURES, ENHANCING THE
MODEL'S ACCURACY.
THIS APPROACH RESULTS AND
PROVIDES INSIGHT INTO INDIVIDUAL
FEATURES AND THEIR INTERACTIONS
ON THE OUTCOME.
WHICH IS IN OUR CASE THE
OBESITY.
AND THAT IS WHY WE CHOSE TO
APPLY THIS MODEL IN OUR STUDY.
IN OTHER PROJECTS,
CONSIDERATIONS APPLY, THEY DO
NOT NECESSARILY PRESENT
DIRECTIONS, MIGHT CHOOSE
DIFFERENT APPROACH.
THE DECISION-MAKING PROCESS IS
CALLED MODEL SELECTION, AND I
BELIEVE IT'S CRUCIAL TO
EMPHASIZE THAT WE HAVE TO STAY
UPDATED WITH THE ADVANCEMENTS IN
THE FIELD.
AND TO INVEST INTO NEW METHODS.
HOWEVER, IT'S ALSO EQUALLY
IMPORTANT TO REMEMBER SIMPLER
APPROACHES SOMETIMES, FOR EARLY
STAGES OF THE MACHINE LEARNING
DEVELOPMENT.
AS BASELINE MODELS OFTEN ARE
PERFORMED MORE COMPLEX ONES,
ESPECIALLY IN DATASETS THAT ARE
BRIDGE BE THE -- BRIDGING THE
GAP LIKE ABCD MODEL.
WE COMBINE TO GAIN MORE
UNDERSTANDING FROM THIS
POPULATION.
NEXT SLIDE PLEASE.
TOP 20 FEATURES RANK FROM TOP TO
BOTTOM, FOR BETTER
INTERPRETABILITY ASSIGNED INTO
COLOR CODED CATEGORIES, YELLOW
REPRESENTING DEMOGRAPHIC
INFORMATION.
IN THIS LIST THE FIRST FIVE
PREDICTORS ARE INTERACTION TERMS
IDENTIFIED BY THE MODEL, NOT US
DEFINING INTERACTIONS.
HOWEVER, THE MODEL POINTED US TO
SIGNIFICANT IMPACT.
YOU CAN ALSO SEE MULTIPLE
INDICATORS OF SES IN THIS LIST
WHICH SHOWS ADOLESCENTS WITH
LOWER SOCIOECONOMIC STATUS ARE
MORE LIKELY TO BE OBESE.
SLEEP PREDICTORS IN PURPLE.
LATER BEDTIME AND HIGHER DAY TO
DAY VARIABILITY, MID-SLEEP
ASSOCIATED WITH OBESITY.
NEXT SLIDE PLEASE.
WE IDENTIFIED CRUCIAL TIPPING
POINTS IN THE PREDICTORS,
SPECIFIC BEHAVIORS BEGAN TO PUT
ADOLESCENTS AT HEIGHTENED RISK
FOR OBESITY.
INTERACTION EMERGED AS TOP RANK
THE SCORE WITHIN THE MODEL.
THIS INTERACTION REVEALS
ELEVATED RISK OF OBESITY AMONG
INDIVIDUALS WHO IDENTIFIED AS
ANY OTHER RACE THAN WHITE, ALSO
HAVE A SLEEPING HEART RATE
GREATER THAN 72 BEATS PER
MINUTE.
CORRESPONDING INCREASE IN
OBESITY RISK AMONG WHITE
INDIVIDUALS, ASSOCIATED WITH
HEART RATE OF 80 BEATS PER
MINUTE OR HIGHER.
SIMILARLY IDENTIFIED
INTERACTIONS BETWEEN SLEEPING
HEART RATE AND TOTAL COMBINED
FAMILY INCOME REVEALING NUANCED
RISK PROFILES.
SPECIFICALLY PARTICIPANTS FROM
FAMILIES WITH INCOME RANGING
BETWEEN FIVE THOUSAND AND
SIXTEEN THOUSAND, ALSO HAD
ELEVATED SLEEPING HEART RATES
WERE FOUNDING TO AT HIGHEST RISK
FOR OBESITY.
CONVERSELY HAVING A FAMILY
INCOME ABOVE $100,000 AND
SLEEPING HEART RATE LOWER
ASSOCIATED WITH DECREASED RISK
OF OBESITY.
IDENTIFYING THESE THRESHOLDS ARE
RELEVANT FOR INTERVENTIONS,
ESPECIALLY DURING DEVELOPMENT
WHEN WE CAN INTERVENE AND
SUPPORT HEALTHY MATURATION.
OUR RESULTS IDENTIFIED AND
CONFIRMED EARLIER BEDTIMES ARE
PROTECTIVE BY THE LATER BEDTIMES
INCREASING RISK OF OBESITY.
SIMILARLY, I COULD LIST
THRESHOLDS FOR EACH PREDICTOR
BUT HOPE YOU GET THE IDEA WE'RE
LOOKING AT PATTERNS RATHER THAN
SOLE PREDICTORS HERE.
THIS SHOWS IMPORTANCE OF
CONTINUOUS MONITORING.
THEY SHOW POTENTIAL METHODS FOR
GLASS BOX MODELS.
AND FOR THE FIRST TIME WE CAN
REALLY HARMONIZE DATASET, ALSO
IDENTIFY PATTERNS AND RISK
THRESHOLDS AND POTENTIALLY
FORECAST FUTURE SYMPTOMS EVEN
BEFORE THE CLINICAL
MANIFESTATION.
INFORMED MODELS WE CAN
CHARACTERIZE SYSTEMS AND RANK
EFFECTS IN A MORE EFFICIENT WAY
THAN BEFORE.
AND I WANT TO MENTION THAT
EXPLOREATIVE MODELS CAN ALSO SET
THE STAGE FOR FOLLOW-UP
ANALYSIS, MORE EXPERIMENTAL
ANALYSES IN THE FUTURE.
AND I WANT TO GIVE AN EXAMPLE
FOR THIS.
NEXT SLIDE PLEASE.
VERY BRIEFLY, APPLIED SIMILAR
APPROACH, TO PREDICT ADOLESCENT
MENTAL HEALTH DURING THE EARLY
STAGES OF PANDEMIC.
IN SITUATIONS LIKE THIS, IT IS
VERY NATURAL THAT WE DON'T HAVE
A CLEAR EXPECTATION ON THE
OUTCOMES.
SO WE MEASURE EVERYTHING THAT WE
CAN AND GOING FOR FISHING
EXPEDITION DECIDED TO OPT FOR
MACHINE LEARNING MODEL, SINCE
THESE ARE DESIGNED FOR
EXPERIMENTAL ANALYSIS AND DO IN
THE INCREASE RISK OF ERROR.
YOU CAN SEE WE IDENTIFIED
PRE-PANDEMIC SLEEP MEASURES
AMONG OTHER THINGS THAT WERE
ASSOCIATED WITH HIGH ANXIETY
LEVELS, ANXIETY SYMPTOMS DURING
THE EARLY STAGES OF THE
PANDEMIC, IN THE ABCD COHORT.
NEXT SLIDE PLEASE.
MODELS REVEAL HOW IMPORTANT
SOCIAL CONNECTIONS WERE DURING
THIS TIME.
TOP PREDICTOR IN THE PREDICTION
OF POSITIVE EFFECT, QUITE
SURPRISING FOR ME, AND THIS DATA
WAS SELF-REPORTED MOSTLY.
THIS IS ALSO THE EARLY FEW
MONTHS OF THE PANDEMIC.
NEXT SLIDE PLEASE.
THIS IS THE LAST EXAMPLE.
APPLIED MACHINE LEARNING BASED
TRAJECTORY ANALYSIS TO IDENTIF
ADOLESCENTS IN THE ABCD STUDY
PERSISTENTLY STRESSED, THE FIRST
SIX -- TIMES SIX -- SORRY,
ACROSS THE FIRST YEAR OF THE
PANDEMIC, SIX TIME POINTS.
THE ABCD STUDY INVITED
PARTICIPANTS TO COMPLETE
SURVEYS, AND YOU ARE IS VASE --
SURVEYS COVERED FIRST YEAR THE
PANDEMIC.
LONGITUDINAL DATA SHOW
TRAJECTORIES IN STRESS FIRST
YEAR OF THE PANDEMIC.
5% OF ADOLESCENTS HAVING HIGH
STRESS LEVELS, ACROSS ALL SIX
ASSESSMENTS, MAY 2020 TO MARCH
2021.
THE HIGH PREVALENCE OF STRESS IN
OUR SAMPLE IS PARTICULARLY
ALARMING, GIVEN THE DURATION OF
STRESS IS CRITICAL FOR LATER
PSYCHOLOGICAL AND BRAIN
FUNCTIONING IN ADOLESCENTS AND
YOUNG ADULTS.
NEXT SLIDE PLEASE.
NEXT STEPS, IDENTIFIED
PRE-PANDEMIC BRAIN AND SLEEP
MEASURES THAT WERE COLLECTED
PRIOR TO THE PANDEMIC AND OTHER
FACTORS, THAT COULD POTENTIALLY
EXPLAIN.
AND THE RESULT HIGHLIGHT ROLE OF
SLEEP AND BRAIN STRUCTURE IN
EARLY ADOLESCENCE, RESILIENCE TO
CHRONIC STRESSORS.
ADDING SLEEP ASSESSMENTS INTO
THE CLINICAL ROUTINE AND ASKING
ADOLESCENTS ABOUT SLEEP WOULD BE
CRITICALLY IMPORTANT FOR EARLY
INTERVENTION.
THESE RESULTS ARE NOT PUBLISHED.
YOU CAN FIND MORE INFORMATION
ABOUT OUR MODELS AND APPROACHES
IN THE PAPERS, AND I'D LIKE TO
SAVE TIME FOR QUESTIONS.
NEXT SLIDE PLEASE.
SO THANK YOU VERY MUCH FOR YOUR
ATTENTION.
THIS IS A TEAM EFFORT.
I'M GRATEFUL TO THE SRI SLEEP
MEMBERS AND ALL THE
COLLABORATORS, I'D LIKE TO THANK
ABCD PARTICIPANTS FOR THEIR
EFFORTS AND ALL THE INSTITUTIONS
WHO SUPPORTED US.
THANK YOU.
>> THANK YOU FOR THE IMPRESSIVE
PRESENTATION.
I'M NOT -- WE HAVE ABOUT FIVE
MINUTES FOR QUESTIONS.
I'M NOT SEEING ANYTHING IN THE
QUAY.
Q&A.
THERE'S A QUESTION THROUGH THE
VIDEOCAST I'M GOING TO -- LET'S
SEE IF I CAN EXTRACT THE SALIENT
POINT, HOW MUCH -- GIVEN THE
AMOUNT OF DATA THE ABCD STUDY
HAS COLLECTED, ARE YOU -- WHAT
SORT OF PRIVACY PROTECTIONS ARE
THERE TO PREVENT SORT OF A.I.
APPROACH FROM UNMASK INDIVIDUALS
IN THIS STUDY, IS THAT SOMETHING
YOU COULD SPEAK TO?
>> SO THE ABCD STUDY DATA IS
PUBLICLY AVAILABLE, AND THERE
ARE MANY CONSIDERATIONS AROUND
PRIVACY.
AND ALL THE DATA USED HERE AND
ANY ANALYSES REGARDING ABCD DATA
IS DE-IDENTIFIED AND DATA IS
COLLECTED, I CAN ASSURE EVERYONE
THAT PRIVACY IS THE BIGGEST
CONCERN AND WE MAKE EVERYTHING
THAT WE CAN TO PROTECT THE
PARTICIPANTS OF THE STUDY.
>> THANK YOU.
LARRY?
>> A COUPLE THINGS.
IN YOUR MODELS DID THEY ALLOW
YOU TO PREDICT OR DETECT
CAUSALITY?
CAN YOU SEE POOR SLEEP CAUSES OR
LEADS TO OBESITY?
>> SO THESE ARE ASSOCIATED
MODELS.
THEY DO NOT POINT TO CAUSALITY.
THAT'S WHY I THINK IT'S
IMPORTANT TO EMPHASIZE THESE
MODELS SET THE STAGE FOR MORE
EXPERIMENTAL APPROACHES.
THEY ARE NUANCED.
WE CAN DESIGN BUILDING UPON
THESE RESULTS.
CAREFULLY CONTROLLED
EXPERIMENTS.
IN THE FUTURE, HOWEVER, THIS
DATA IS NOT MEANT TO BE CAUSAL,
POINT TO CAUSALITY IN THE
FUTURE, START FORECASTING THE
FUTURE I THINK WE GET A CHANCE
TO BE A LITTLE BIT MORE
CONFIDENT IN OUR -- IN THE
IMPACT OF THE PREDICTORS.
HOWEVER, AT THIS POINT I WOULD
SAY, NO, THIS IS NOT MEANT TO BE
CAUSAL.
>> OKAY.
CLEARLY THERE'S CONCERN OBESITY
IS INCREASING ON AMONG
ADOLESCENTS, EPIDEMIC
PROPORTIONS, NOT SURE IF ABCD
HAS THE CAPABILITY TO DETECT
THAT, LOOK AT THOSE TRENDS OVER
TIME.
>> ABCD IS LOOKING AT TRENDS
OVER TIME.
THIS IS A STUDY FOR TEN YEARS,
VERY GOOD RETENTION RATES.
AND WE'RE LOOKING INTO THE
TRAJECTORIES.
THE DATA IS CURRENTLY AVAILABLE
DATA, COVERS THE FIRST THREE
YEARS AND A HALF AND HOPEFULLY
SOON THE FIRST FOUR YEARS OF THE
ABCD STUDY, AND WE KEEP GOING
COLLECTING DATA SO, YES, I THINK
YOU CAN SAY IN THE FUTURE WE'LL
HAVE A LOT MORE CLEAR
UNDERSTANDING ABOUT THE
DEVELOPMENT OF OBESITY.
>> ONE MORE QUICK THING.
>> YES, PARTICIPANTS WERE
ENROLLED, THEY BECAME NINE AND
TEN YEARS OLD AND WE'RE
FOLLOWING THEM TO YOUNG
ADULTHOOD HOPEFULLY.
>> TODD, THERE'S A QUESTION IN
THE CHAT IF YOU WANT TO RELAY
IT.
>> YEAH, SO ONE QUESTION IS WHAT
SLEEP REGULARITY MEASURES DID
YOU USE TO DEVELOP THE SLEEP
TIMING ASSOCIATIONS?
>> VERY GOOD QUESTION.
THIS IS A MACHINE LEARNING
APPROACH SO I'M NOT LIMITED TO
CALCULATING A FEW PREDICTORS AND
I CALCULATE VARIABILITY MEASURES
CONSIDERING WIDE RANGE OF
POTENTIAL WEIGHT, MOST RELEVANT
ONES IN THIS PARTICULAR
ANALYSIS, MAIN SEQUENTIAL
STANDARD ERROR THAT COVERS
DAY-TO-DAY VARIABILITY QUITE
WELL, BUT IN SOME DEFINING
VARIABILITY DIFFERENTLY,
SOMETIMES USING STANDARD
DEVIATION EVEN.
>> THANK YOU.
ONE MORE QUICK QUESTION.
CAN YOU SPEAK TO THE
ASSOCIATIONS BETWEEN SCREEN TIME
AND SLEEP OUTCOMES IN THE ABCD?
>> YES.
ACTUALLY IN BETWEEN THOSE, IF WE
COULD SEE THAT THE SCREEN USE
ESPECIALLY DURING THE PANDEMIC
VERY IMPORTANT IN PREDICTOR OF
MENTAL HEALTH, SO WE DO HAVE
LOTS OF FOLLOW-UP STUDIES THAT
SHOW SCREEN USE IN ADOLESCENTS
HAS A VERY IMPORTANT ROLE IN
MENTAL HEALTHMENT .
WE HAVE LOTS OF STUDIES
PUBLISHED AROUND THOSE
QUESTIONS, HOPING TO APPLY
MACHINE LEARNING.
THIS POPULATION IS NOT WELL
CHARACTERIZED.
>> THANK YOU.
WE HAVE TO MOVE ON.
THANK YOU VERY MUCH, DR. KISS.
NEXT SPEAKER ADAM SPIRA,
PROFESSOR AND VICE CHAIR FOR
RESEARCH AND FACULTY, DEPARTMENT
OF MENTAL HEALTH, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC
HEALTH.
JOINT APPOINTMENT IN DEPARTMENT
OF PSYCHIATRY AND BEHAVIORAL
SCIENCES AT THE JOHNS HOPKINS
SCHOOL OF MEDICINE.
CORE FACULTY OF THE JOHNS
HOPKINS CENTER ON AGING AND
HEALTH.
TRAINING IN CLINICAL PSYCHOLOGY
AND EPIDEMIOLOGY, RESEARCH
FOCUSES ON ASSOCIATION OF SLEEP
DISTURBANCE WITH COGNITIVE AND
FUNCTIONAL DECLINE IN
ALZHEIMER'S DISEASE.
HE WILL BE SPEAKING ABOUT BIG
DATA AND SLEEP, FINDINGS ACROSS
ADULTHOOD.
>> THANK YOU VERY MUCH.
THANKS FOR THE INVITATION.
NEXT SLIDE PLEASE.
SOME DISCLOSURES TO REVIEW
BRIEFLY.
NEXT SLIDE PLEASE.
SO, AS YOU LIKELY KNOW, THE
POPULATION IS AGING.
IF YOU LOOK AT THIS TABLE ON THE
LEFT, YOU CAN SEE THAT
BEGINNING -- IF YOU LOOK AT 2012
OVER 40 MILLION PEOPLE AGE 65
AND OLDER IN THE UNITED STATES.
AND BY 2050 PROJECTED TO DOUBLE
BEYOND 80 MILLION.
I RECENTLY SAW A NEWER FIGURE
EVEN HIGHER THAN THAT.
THIS IS IMPORTANT WITH RESPECT
TO DEMENTIA, BECAUSE DEMENTIA IS
PREVALENT AMONG OLDER ADULTS.
ALZHEIMER'S DISEASE IS THE MOST
COMMON CAUSE.
IT'S CHARACTERIZED BY
EXTRACELLULAR BETA AMYLOID
PRACTICALS, INTRANEURONAL
FIBRILLARY TANGLES, MADE OF TAU,
PHOSPHORYLATED TAU, AND IT'S
ESTIMATED THAT IN 2010 THERE
WERE 4.7 MILLION U.S. ADULTS 65
AND OLDER WITH A.D. AND BY 2050
WE EXPECT TO SEE ALMOST 14
MILLION.
AND IT'S CRITICALLY IMPORTANT AS
A PUBLIC HEALTH CONCERN BECAUSE
THERE'S NO CURE FOR ALZHEIMER'S
DISEASE AND DEMENTIA.
SO PREVENTION IS CRITICAL.
NEXT SLIDE PLEASE.
SO YOU MIGHT ASK, WELL, WHAT
DOES THIS HAVE TO DO WITH SLEEP?
AND SLEEP DISTURBANCES ARE
COMMON IN OLDER ADULTS BROADLY.
POOR SLEEP IS INCREASINGLY
LINKED TO POOR LATER LIFE HEALTH
OUTCOMES, INCLUDING COGNITIVE
DECLINE, FUNCTIONAL DECLINE,
ALZHEIMER'S DISEASE PATHOLOGY
AND DEMENTIAS DUE TO ALZHEIMER'S
DISEASE AND ALZHEIMER'S
DISEASE-RELATED DEMENTIA
DISEASES.
SLEEP DISTURBANCE IS TREATABLE
OFTEN, MAKING SLEEP DISTURBANCE
POTENTIAL MODIFIABLE RISK FACTOR
FOR DEMENTIA.
NEXT SLIDE PLEASE.
PEOPLE HAVE THOUGHT ABOUT THE
LINK BETWEEN NEURODEGENERATIVE
DISEASE AND SLEEP MODEL, THIS IS
LIKELY BECAUSE OF THE
SIGNIFICANT SALIENT SLEEP/WAKE
PROBLEMS OBSERVED AMONG PEOPLE
WITH CLINICAL ALZHEIMER'S
DISEASE AND RELATED DEMENTIA, A
MAJOR STRESSOR FOR DEMENTIA,
FAMILY CAREGIVERS, ONE OF THE
REASONS THAT THOSE CAREGIVERS
GIVE FOR HAVING PLACED THEIR
LOVED ONES IN A LONG-TERM CARE
FACILITY.
COMMONLY.
AND SO IT'S NOT SURPRISING THAT
THIS HAS BEEN THE MOST OBVIOUS
WAY TO THINK ABOUT THE LINK
BETWEEN NEURODEGENERATIVE
DISEASE AND SLEEP/WAKE
DISTURBANCE.
NEXT SLIDE PLEASE.
HERE YOU SEE AN OLD EXAMPLE OF
SOME BIG DATA FROM
ACCELEROMETRY, A SMALL END STUDY
MORE OF A CASE STUDY.
AT THE TOP WE SEE THIS TIME
SERIES OF ACCELEROMETER DATA
WITH ACTIVITY COUNTS REPRESENTED
BY THESE BLACK BARS, VERTICAL
BARS, OVER A NIGHT.
IN THE TOP PANEL YOU SEE A
REALLY FRAGMENTED PATTERN,
WITH -- IT'S HARD TO FIND
PERIODS OF REST OR CONTINUOUS
PERIODS OF ACTIVITY HERE.
THERE'S A LOT OF FRAGMENTATION.
BOTTOM PANEL YOU CAN SEE A
PERIOD OF RELATIVE QUIET,
BEHAVIORAL OR MOTOR IMMOBILITY
BETWEEN AROUND 11:30 P.M. AND
4:30 OR SO A.M.
AND WE MIGHT ASSUME THAT HAS TO
BE -- LIKELY WHERE SLEEP
OCCURRED BUT THIS IS JUST TO
ILLUSTRATE SLEEP/WAKE
DISTURBANCES CAN CERTAINLY BE
PROMINENT IN PEOPLE WITH
DEMENTIAING ILLNESS LIKELY I DO
OF -- DUE IN PART TO CHANGES IN
THE BRAIN.
NEXT PLEASE.
A STUDY IN ALZHEIMER'S DISEASE
MOUSE MODEL BY ROWE AND
COLLEAGUES FROM THE GROUP AT
WashU SHOWED IN AN A.D. MOUSE
AS AMYLOID BEGINS TO AGGREGATE
THERE'S A LOSS OF THE SLEEP/WAKE
CYCLE.
SO, AGAIN, NOT SURPRISING THAT
PEOPLE WOULD START TO -- WOULD
THINK ABOUT BRAIN CHANGES
LEADING TO POOR SLEEP.
HOWEVER, IF YOU CLICK ON THE
NEXT SLIDE PLEASE, THERE STARTED
TO EMERGE STUDIES IN THE LAST 20
YEARS THAT REALLY HAVE CHANGED
THE WAY WE'VE BEEN THINKING
ABOUT SLEEP DISTURBANCE AND
ALZHEIMER'S DISEASE.
THIS IS A FIGURE FROM A PAPER BY
ANDREW LIM AND COLLEAGUES.
IN 737 ADULTS AGE 8 2 AT
BASELINE, FOLLOWED WITH
ACCELEROMETRY AND DEVELOPED A
FRAGMENTATION INDEX, IT'S A
PROBABILISTIC EQUATION THAT
LOOKS AT PROBABILITY OF
TRANSITIONING FROM REST TO
ACTIVITY.
AND INFERS SLEEP FRAGMENTATION
FROM THAT.
OVER 3.3 YEARS OF FOLLOW-UP THEY
SHOWED THAT FOR EVERY STANDARD
DEVIATION INCREASE IN THIS KRA
METRIC, RISK OF INCIDENT
ALZHEIMER'S DEMENTIA DIAGNOSED
CLINICALLY USING STRICT RESEARCH
STANDARDS INCREASED BY 22% PER
INCREASE OF KRA METRIC, SHOWING
IF YOU LOOK AT THE SOLID LINE,
THAT REPRESENTS PEOPLE IN THE
90th PERCENTILE OF THIS KRA
FRAGMENTATION, AND THEY HAD 1
1/2 TIMES THE RISK OF INCIDENT
ALZHEIMER'S DEMENTIA COMPARED TO
THOSE WITH TENTH PERCENTILE IN
THE DOTTED LINE.
NEXT SLIDE PLEASE.
SO, THIS IS -- THE ONE ON LEFT
SAYING SLEEP FRAGMENTATION,
SUPPOSED SLEEP FRAGMENTATION,
BAD BRAIN HEALTH OUTCOME,
DEMENTIA.
IN THE MIDDLE A PAPER THAT I
CO-AUTHORED IN 2011, LOOKED AT
THE ASSOCIATION BETWEEN SLEEP
DISORDERED BREATHING AND
SUBSEQUENT MCI OR DEMENTIA, IN
OLDER WOMEN.
STUDY OF OSTEOPOROTIC FRACTURE
IT'S.
THERE WERE 298, MEAN AGE OF 82.
MEDIAN AHI OF 10.
35% OF THEM HAD AN AHI OF 15+
WHICH WE CONSIDERED SLEEP
DISORDERED BREATHING TO BE
POSITIVE FOR SVB.
AND COMPARED TO THOSE WHO DID
NOT HAVE SVB, THEY HAD 85%
GREATER ODDS OF HAVING MCI OR
DEMENTIA, FIVE YEARS LATER.
AND IT REALLY APPEARED TO BE
HYPOXEMIA THAT EXPLAINED THAT
ASSOCIATION.
WE DIDN'T SEE ASSOCIATIONS OF
SLEEP DURATION OR SLEEP
FRAGMENTATION WITH THAT OUTCOME.
SO SLEEP DISORDERED BREATHING,
NOT SURPRISING, BAD FOR THE
BRAIN, THE TAKEAWAY THERE.
ALONG WITH A LOT OF OTHER
STUDIES IN THAT DIRECTION.
YOU LOOK AT THE STUDIES LIKE
THIS, YOU THINK TO YOURSELF,
WELL, COULDN'T THIS BE CHANGES
IN THE BRAIN ARE LEADING TO BOTH
POOR SLEEP PROBLEMS, POOR SLEEP,
BROADLY, EVEN RESPIRATORY
CHANGES DURING SLEEP, AND POOR
COGNITIVE OUTCOMES.
YES, THAT'S PLAUSIBLE.
BUT IN 2009, NEXT SLIDE PLEASE,
THE GROUP AT WashU SHOWED US
IF YOU LOOK AT THE TOP FIGURE
HERE IF YOU SLEEP DEPRIVE
ALZHEIMER'S DISEASE MOUSE MODEL
YOU'LL GET MARKEDLY GREATER
AMYLOID PLAQUE BURDEN IN THE
BRAINS OF THOSE MICE.
AND THE BLACK BARS INDICATE THE
PERCENT PLAQUE BURDEN IN SLEEP
RESTRICTED MICE AND WHITE BARS
ARE THE CONTROLS.
AND THIS WAS EXCITING BECAUSE IT
PROVIDED THE FIRST EVIDENCE OF A
CAUSAL LINK BETWEEN INSUFFICIENT
SLEEP, SLEEP LOSS, OR SLEEP
RESTRICTION I GUESS, AND
ALZHEIMER'S DISEASE.
IN MARK WU'S LAB AT JOHNS
HOPKINS, A STUDY IN 2015 SHOWING
IN DROSOPHILA MODEL OF SLEEP OF
ALZHEIMER'S SHE'S, SORRY,
DROSOPHILA MODEL OF ALZHEIMER'S
DISEASE, THAT IF YOU SLEEP
DEPRIVE THESE FLIES, YOU GET
MARKEDLY GREATER AMYLOID
DEPOSITION IN THEIR BRAINS, THAN
IF YOU DON'T.
BOTTOM SLIDE, THAT'S WHEN THE
TUBES THEY LIVE IN ARE SHAKEN
DURING THE NIGHT HOURS WHEN THEY
WOULD ORDINARILY BE ASLEEP AND
AT THE TOP IS THE -- REPRESENTS
THE BRAIN OF THE FLIES WHO HAD
TUBES SHAKEN DURING THE DAY WHEN
THEY WERE AWAKE ANYWAY.
FURTHER EVIDENCE IN A DIFFERENT
SPECIES OF POTENTIAL CAUSAL
LINK.
GROWING LITERATURE INDICATES NOT
JUST A LINK OF NEURODEGENERATIVE
DISEASE TO SLEEP/WAKE
DISTURBANCE BUT ALSO, NEXT SLIDE
PLEASE, LINK IN THE OTHER
DIRECTION.
NOW WE KNOW THERE'S MORE STUDIES
COMING OUT TO SUPPORT A
BIDIRECTIONAL LINK BETWEEN
SLEEP/WAKE DISTURBANCE AND
ALZHEIMER'S DISEASE.
AND TODAY I'LL SHARE WITH YOU A
COUPLE OF STUDIES THAT I'VE BEEN
INVOLVED WITH THAT LEVERAGE BIG
DATA, USING POPULATION BASED
DATA, ANOTHER FROM
ACCELEROMETERS TO INVESTIGATE
LINKS BETWEEN SLEEP AND
DEMENTIA.
THIS IS A STUDY I COLLABORATED
ON WITH COLLEAGUES IN DENMARK
USING DATA FROM THE DANISH
NATIONAL PATIENT REGISTRY,
PSYCHIATRIC CENTRAL RESEARCH
REGISTER AND DANISH NATIONAL
PRESCRIPTION REGISTRY.
WE WANTED TO STUDY ASSOCIATION
OF SLEEP DISORDER DIAGNOSIS IN
MID-LIFE STARTING IN MID-LIFE,
FROM AGE 50 ON, WITH RISK OF
DEMENTIA AT AGE 65 AND OLDER.
WE CLOSE THAT BECAUSE THAT'S
LATE ONSET DEMENTIA, WHICH IS
BELIEVED TO BE A DISTINCT
DISEASE ENTITY OR COLLECTION OF
ENTITIES FROM EARLY ONSET
DEMENTIA.
WE STUDY PEOPLE IN THAT AGE
RANGE WITH REGISTERED DATA AFTER
EXCLUDING THOSE WHO DEVELOP
DEMENTIA IN THIS BIRTH COHORT
BEFORE THE AGE OF 65.
YOU CAN SEE SO ONE THING I WANT
TO POINT OUT IS THAT SLEEP
DISORDER DIAGNOSES IN THESE DATA
WERE ONLY AVAILABLE IN RECORDS
FROM HOSPITAL CONTACTS INCLUDING
OUTPATIENT VISITS, BUT THEY ARE
NOT PRIMARY CARE.
THIS IS A LIMITATION OF THE
STUDY THAT HAS TO BE CONSIDERED
WHEN WE INTERPRET THE RESULTS.
WE WANTED TO LOOK AT SEX
DIFFERENCES, DUE TO KNOWN SEX
DIFFERENCES IN BOTH SLEEP
DISTURBANCE AND IN DEMENTIA
RISK.
WE ENDED UP WITH JUST ABOUT 1.5
MILLION PEOPLE TO STUDY.
BASED ON ICD 8 AND 10 CODES, ONE
OF THEM, BROAD CATEGORIES WERE
SLEEP APNEA, NARCOLEPSY AND
CATAPLEXY, INSOMNIA VERY RARE AS
A DIAGNOSIS FROM THE HOSPITAL
RECORD, HYPERSOMNIA, DISORDERS
OF THE SLEEP/WAKE SCHEDULE,
PARASOMNIA, SLEEP-RELATED
MOVEMENT DISORDERS INCLUDING
PERIODIC LIMB MOVEMENT DISORDER,
BRUXISM, AND LEG CRAMPS.
NON-ORGANIC SLEEP DISORDERS, AND
OTHER -- SORRY, NON-ORGANIC
SLEEP DISORDERS.
ANOTHER CATEGORY, UNSPECIFIED
SLEEP DISORDERS.
THAT INCLUDED OTHER SLEEP
DISORDERS, AND WHICH WERE NOT IN
THE OTHER SPECIFIC SLEEP
DISORDERS GROUP, AND UNSPECIFIED
SLEEP DISORDER.
SO, I WANT TO POINT OUT THE SEX
DIFFERENCE HERE.
YOU CAN SEE MEN WERE MUCH MORE
LIKELY TO HAVE A SLEEP DISORDER
THAN WOMEN IN THIS STUDY.
THIS WAS LIKELY AMPLIFIED BY THE
NEAR ABSENCE OF INSOMNIA
DIAGNOSES FROM THESE REGISTERS.
INSOMNIA IS TREATED MORE
FREQUENTLY IN THE PRIMARY CARE
SETTING, FROM WHICH REGISTRY
DATA WERE NOT AVAILABLE.
NEXT SLIDE PLEASE.
AND WHAT YOU CAN SEE, WE
COMPUTED INCIDENT RATIOS FOR
DEMENTIA, ADJUSTED FOR AGE, SEX,
CALENDAR YEAR, EDUCATION,
SOMATIC COMORBIDITY, WE PUT
TOGETHER A CHARLESTON
COMORBIDITY INDEX AND
PSYCHIATRIC CO-MORBIDITY.
ANY SLEEP DISORDER, 17% GREATER
RISK OVER FOLLOW-UP OF
DEVELOPING DEMENTIA IN THOSE
ADJUSTED ANALYSES.
BY SEX YOU CAN SEE THIS WAS
DRIVEN MUCH MORE STRONGLY BY MEN
AND THEN WE LOOKED AT
NARCOLEPSY, WE DID NOT FIND A
SIGNIFICANT ASSOCIATION THAT
COULD HAVE BEEN DUE TO THE
RELATIVELY LOW NUMBER OF PEOPLE
WITH DIAGNOSED NARCOLEPSY IN THE
SAMPLE.
BREATHING WAS ASSOCIATED WITH
13% GREATER ODDS OF DEMENTIA,
REALLY DRIVEN BY MEN, ONLY IN
MEN.
OTHERS INCLUDED, WELL, THE MOST
STRONGLY ASSOCIATED WITH
DEMENTIA.
IF YOU HAD ANOTHER SPECIFIC
DISORDER YOU HAD 80% GREATER
RISK OF DEVELOPING DEMENTIA,
THIS MAY WELL HAVE BEEN DRIVEN
BY PARASOMNIA.
THE CATEGORY DROVE THIS ALMOST
ENTIRELY.
AND IT WAS -- WE KNOW REM SLEEP
BEHAVIOR FITS IN THAT CATEGORY,
THAT MAY BE WHAT'S ACCOUNTING
FOR THIS.
AND NO ASSOCIATION OF
UNSPECIFIED DISORDER.
THIS WAS AN INTERESTING EXERCISE
FOR ME, SLEEP RESEARCHER, JUST
GETTING INTO THIS ASPECT OF BIG
DATA, REALLY LARGE NUMBERS.
AND REST BLING WITH SOME OF THE
LIMITATIONS OF CERTAIN DATA
SOURCES.
AND THAT'S SOMETHING WE CAN TALK
ABOUT MORE.
BIG DATA ON A SMALL KALE, IT'S
TWO STUDIES.
NEXT SLIDE PLEASE.
THIS IS A STUDY WE DID, PAUL
ROSENBERG AND I, RECEIVED
FUNDING FROM NIA.
THANK YOU, NIA.
TO ADD RISK ACTI GRAPHY TO THE
A4 TRIAL, IT WAS A RANDOMIZED
TRIAL OF A MONOCLONAL ANTIBODY
FROM THE BASELINE PHASE OF THE
TRIAL.
AS YOU KNOW, THEY REAL
VOLUMINOUS DATA AND WE WANTED TO
LEVERAGE THOSE DATA TO EVALUATE
SLEEP AND 24-HOUR REST ACTIVITY
RHYTHM DATA.
SOME PEOPLE CAUGHT IT CIRCADIAN
POTENTIAL MARKERS OF
PRE-CLINICAL AMYLOID DEPOSITION
IN THE CONTEXT OF THIS
RANDOMIZED TRIAL BASELINE.
AND WE COMPARED STANDARD
CIRCADIAN METRICS INCLUDING --
THIS WAS 59 COGNITIVELY NORMAL,
THIS WAS AN IMPORTANT PIECE, AND
IF THEY A-BETA DEPOSITION WERE
ENROLLED IN THE A4 TRIAL.
IF THEY WERE NEGATIVE FOR BETA
AMYLOID ON PET SCAN, THEY WERE
PUT INTO THE LEARN COHORT,
LONGITUDINAL EVALUATION OF
AMYLOID RISK AND DEGENERATION
STUDY.
WE USED THE A-BETA POSITIVE
PEOPLE, AND DATA FROM NEGATIVE
FOLKS FROM THE LEARN STUDY FOR
THIS AND COMPARED TOTAL SLEEP
TIME, SLEEP EFFICIENCY, SLEEP
ONSET LATENCY.
NON-PARAMETRIC INDICES.
YOU CAN SEE DESCRIPTION OF THE
SAMPLE ON THE RIGHT.
THE IMPORTANT THING TO NOTE HERE
IS THERE WERE NO DIFFERENCES
BETWEEN USING THESE STANDARD
METRICS, WHICH TEND TO AVERAGE
OVER A WEEK OF DATA COLLECTION
OR SO, CAN OBSCURE SOME
IMPORTANT DIFFERENCES
POTENTIALLY THAT ARE OCCURRING
IN A FINER GRAIN LEVEL IN DATA.
SO WE ALSO WANTED TO APPLY
FUNCTION ON SCALE OF REGRESSION,
A DATA DRIVEN NON-PARAMETRIC
APPROACH TO IDENTIFY POTENTIAL
DIFFERENCES BETWEEN THESE
GROUPS.
AND MANY THANKS TO MY CO-AUTHOR,
AS ALWAYS A TREMENDOUS HELP WITH
THIS STUFF.
WE FOUND INTERESTING THINGS,
REFRESHING BECAUSE WE FOUND
NOTHING USING STANDARD METRICS.
>> TWO MINUTES LEFT.
>> OKAY.
NEXT SLIDE PLEASE.
IF YOU LOOK ACROSS 30-MINUTE
INTERVALS IN THE DAY, THE TIME
OF DAY GOES ACROSS THE X-AXIS,
OKAY?
AND THE RED CURVE IS THE A-BETA
POSITIVE FOLKS, BLUE IS A-BETA
NEGATIVE FOLKS.
VERTICAL BARS INDICATE TIMES OF
DAY DIFFERENCE IN ACTIVITY
COUNTS BETWEEN TWO GROUPS
SIGNIFICANT AFTER ADJUSTING FOR
AGE, SEX, EDUCATION.
AND THE PINK BARS INDICATE
SIGNIFICANCE USING CONSERVATIVE
STATISTICAL METHOD THAT ACCOUNTS
FOR THE CORRELATION OF ADJACENT
TIME POINTS.
BLUE BARS ARE FROM THE LESS
CONSERVATIVE APPROACH.
YOU SEE BROADER INTERVALS OF
SIGNIFICANT DIFFERENCES.
WHAT WE NOTED IS IF YOU LOOK
BETWEEN 12 P.M. ROUGHLY AND, OH,
17, 1800, AROUND 6 P.M., HIGHER
LEVEL OF ACTIVITY IN THE RED
BAR, THAN IN THE BLUE GROUP.
WE SAW STARTING JUST BEFORE
MIDNIGHT LOWER LEVEL OF
ACTIVITY, FOLKS ALL COGNITIVELY
NORMAL, AND COMPARED TO THE BLUE
GROUP, AND THIS REVERSED
DIRECTIONS IN STARTING AROUND 5
A.M. TO 8 A.M.
THIS IS USING THE MORE
CONSERVATIVE SIGNIFICANCE
THRESHOLD.
WE LOOKED AT VARIABILITY AND SAW
SEVERAL FINDINGS.
SO THERE WAS -- THE ONE I WANT
TO POINT OUT THAT WE SAW A BUNCH
OF TIMES DURING WHICH THERE WERE
HIGHER OR LOWER LEVELS OF
VARIABILITY IN ACTIVITY ACROSS
DAYS, BETWEEN THOSE WHO HAD
A-BETA IN THEIR BRAIN AND THOSE
WHO DID NOT.
I WANT TO DRAW YOUR ATTENTION TO
THE ONE THAT'S ON THE RIGHT-HAND
SIDE, EXCLUSIVELY BLUE.
LOWER VARIABILITY VERSUS THOSE
WHO DID NOT.
THESE WERE POST HOC ANALYSES AND
NEEDED TO BE REPLICATED SO WE
DID THAT IN AN INDEPENDENT
COHORT USING THE SAME
ACCELEROMETERS ON THE WRIST, AND
DIFFERENT RADIO TRACER,
INDEPENDENT SAMPLE.
SO WHAT DO WE FIND?
NEXT SLIDE PLEASE.
WE PUBLISHED THIS IN SLEEP, THE
ONE PREVIOUSLY WAS SLEEP
ADVANCES, THIS ONE IN SLEEP.
WHAT WE SHOWED, 82 COGNITIVELY
NORMAL ADULTS, AGAIN, SOME
A-BETA POSITIVE, SOME NEGATIVE,
GREATER ACTIVITY IN THIS
AFTERNOON PERIOD IF YOU LOOK ON
PANEL A AMONG THOSE WHO HAD --
WERE POSITIVE FOR A-BETA ON A
PITTSBURGH COMPOUND B SCAN.
AND THAT WAS SORT OF SATISFYING
BECAUSE IT POINTED TO -- THIS IS
A SMALLER PERIOD WE SAW
SIGNIFICANT DIFFERENCE IN THE
OTHER STUDY, IN THE SAME TIME OF
DAY WHERE WE SAW THIS DIFFERENCE
IN ACTIVITY LEVELS.
IF YOU LOOK AT PANEL B THAT'S
THE VARIABILITY.
THE ONLY ONE THAT OVERLAPPED
WITH THE ONE WE SAW BEFORE WAS
THIS SAME PATTERN OF REDUCED
VARIABILITY IN ACTIVITY COUNTS
IN THIS AFTERNOON PERIOD, THAT
OVERLAPS IN PART WITH PERIOD OF
ELEVATED ACTIVITY AMONG THOSE
WHO HAVE A-BETA IN THEIR BRAINS.
I THINK THIS POINTS TO THE VALUE
OF BIG DATA OR SMALL BIG DATA
AND DATA-DRIVEN APPROACHES TO
SCIENTIFIC DISCOVERY THAT ARE
AFFORDED BY THE HIGH DIMENSIONAL
DATA WE GET FROM SLEEP MEASURES
IN GENERAL.
NEXT SLIDE PLEASE.
BRIEFLY, WHAT ARE FUTURE
DIRECTIONS?
MANY OTHER PEOPLE ARE DOING WORK
THAT IS FANTASTIC IN THIS AREA.
FORGIVE ME FOR SHOWING ONLY MY
OWN HERE.
WE NEED TO MOVE BEYOND AVERAGES
AND MAIN EFFECTS TO CHARACTERIZE
RISK.
BY MOVING BEYOND AVERAGES I MEAN
TWO THINGS.
IN THE SLEEP AND ALZHEIMER'S
DISEASE AREA, THE FIRST IS
MOVING BEYOND POPULATION
AVERAGES TO INVESTIGATE
MODERATORS.
NOT JUST MAIN EFFECTS BUT
LOOKING AT FACTORS THAT MODIFY
WITH DEMENTIA, INCLUDING SEX
DIFFERENCE OR THOSE BASED ON
FINER GRAINED DEMOGRAPHIC AND
CLINICAL CHARACTERISTICS OR
CHARACTERISTICS IDENTIFIED BY
DATA DRIVEN METRICS, OR A.I.
WHO IS AT THE RISK, MOST AT
RISK, OF SLEEP DISTURBANCE
INDUCED DEMENTIA?
THE SECOND THING I MEAN BY
MOVING BEYOND AVERAGES IS MOVING
BEYOND CONVENTIONAL COURSE
METRICS MEASURED OVER FOUR OR
FIVE NIGHTS THAT WE CONVENTIONAL
USE.
I THINK THAT IN THIS FIELD WE
HAVE -- THERE'S SUCH POTENTIAL
TO LEVERAGE PSG AND WEARABLE
DATA FOR PREDICTION, FOR
PREVENTION BY IDENTIFYING PEOPLE
AT RISK WHO MIGHT NEED MORE
INTERVENTION, IN ORDER TO
PREVENT, AND FOR MONITORING OF
TREATMENTS AS OTHER PEOPLE
PRESENTED TODAY.
AND THEN FINALLY I THINK IT'S
CRITICAL WE COLLABORATE AND
SHARE DATA AND WORK TOGETHER TO
HARMONIZE AND STANDARDIZE, SO
THAT'S IT.
MY LAST SLIDE PLEASE.
JUST A THANK YOU.
FIRST TO THE ORGANIZERS, FOR
HAVING ME PRESENT TODAY.
AND TO NATIONAL INSTITUTE ON
AGING, SUPPORTED ME SINCE MY K
AWARD AND FANTASTIC.
MY CO-AUTHORS AND COLLABORATORS,
TRAINEES, PARTICIPANTS WHO MAKE
THIS POSSIBLE.
THANK YOU.
>> THANK YOU.
WE'RE RUNNING BEHIND AND LET'S
SAVE QUESTIONS FOR THE OVERALL
Q&A FOR THIS SESSION.
AND SO LET'S MOVE ON TO DR.
MORRIS, ASSISTANT PROFESSOR AT
THE UNIVERSITY OF PITTSBURGH
SCHOOL OF NURSING, PRIMARY GOAL
OF THE RESEARCH PROGRAM TO
GENERATE KNOWLEDGE TO IMPROVE
CARE AROUND CLINICAL OUTCOMES OF
WOMEN WITH OBSTRUCTIVE SLEEP
APNEA, FOCUSED ON
MULTI-FACTORIAL INFLUENCES OF
GENDER THAT AFFECT PRESENTATION
IN WOMEN, AND SUCCESS WITH SLEEP
SPEAKING ON SEX DIFFERENCES IN
OBSTRUCTIVE SLEEP APNEA.
PLEASE GO AHEAD, DR. MORRIS.
>> THANK YOU FOR THE
INTRODUCTION.
I'M ASSISTANT PROFESSOR AT THE
UNIVERSITY OF PITTSBURGH SCHOOL
OF NURSING, HONORED TO BE HERE
TODAY.
AND I HAVE -- HOPING YOU WILL
HAVE A GREATER UNDERSTAND OF HOW
SEX DIFFERENCES ARE CONTRIBUTING
TO HETEROGENEITY.
SO SINCE YESTERDAY, MY PLAN FOR
TODAY IS TO INTRODUCE TOPIC
EFFECTS IN HEALTH CARE, A REALLY
BRIEF OVERVIEW OF HOW OTHER
CONDITIONS HAVE LOOKED AT SEX
DIFFERENCES IN BIG DATA, AND
THEN GIVE YOU IDEAS FOR HOW
THAT MIGHT BE DONE, A FEW
EXAMPLES FROM WORK AND LASTLY
WILL GO OVER SOME OF THE
CHALLENGES THAT WE MIGHT
EXPERIENCE, SOMEBODY MENTIONED
YESTERDAY FROM LATENT BIASES,
I'LL HIT ON SOME OF THOSE AT THE
END.
I WANT TO TELL YOU WHAT I MEAN
ABOUT SEX AND GENDER.
FOR GENDER I'M REFERRING TO
SOCIETAL ROLES, EXPECTATIONS,
POWER STRUCTURES DIFFERENTIALLY
IMPACT WOMEN'S AND MEN'S HEALTH.
I WILL BE USING SEX DIFFERENCES
BROADLY DIFFERENCES BETWEEN MEN
AND WOMEN.
FACTORS AFFECTED WOMEN'S HEALTH,
IN TERMS OF WOMEN'S REPRODUCTIVE
LIFESPAN, PUBERTY, PREGNANCY,
MENOPAUSE, LESS ATTENTION PAID
TO EXTERNAL FACTORS THAT AFFECT
WOMEN'S HEALTH SUCH AS SOCIAL
DETERMINANTS OF HEALTH,
BEHAVIORS, ENVIRONMENT,
POLICIES, ET CETERA, MY INTEREST
HAS BEEN IN THE IMPACT OF
GENDER.
NOT JUST GENDER SOCIETAL ROLES
BUT HOW GENDER ITSELF IS
INFLUENCING RESEARCH QUESTIONS
THAT WE'VE BEEN ASKING IN OSA
RESEARCH.
NEXT SLIDE PLEASE.
SO, I WANT TO REVIEW A FEW
SLIDES.
I APPRECIATE DR. SPIRA
STRATIFYING BY SEX.
I WANT TO SHOW MORE WHAT'S GOING
ON.
U.K. BIOBANK DATA, WOMEN WITH
HEART DISEASE MORE LIKELY TO
HAVE ALL-CAUSE DEMENTIA.
SEX DIFFERENCE RESEARCH,
STANFORD HOSPITAL AND CLINIC,
PAIN DATA.
THEY LOOKED AT SEX DIFFERENCES
AND PAIN REPORTS.
160,000 DATA POINTS, 250,000
DIAGNOSES, FOUND SEX
DIFFERENCES, BIG DATA CAN ALLOW
US TO DRILL DOWN AND SEE THESE
EFFECTS.
LAST STUDY HERE, LONGITUDINAL
SURVEYS, U.S. HEALTH AND
RETIREMENT STUDY, CHINA HEALTH
AND RETIREMENT LONGITUDINAL
STUDY.
THE POINT I WANT TO MAKE YOU CAN
SEE IN THE U.S. SIDE THAT MORE
MEN HAVE HEART DISEASE AT ANY
AGE BUT IN CHINA IT LOOKS LIKE
THE WOMEN HAVE MORE HEART
DISEASE.
POINTING TO CULTURAL FACTORS
THAT MIGHT BE DIFFERENT.
WE WANT TO TAKE INTO MIND
DIFFERENCES, SEX DIFFERENCES
WITHIN CULTURES.
SO, NEXT SLIDE PLEASE.
THE WORK IN CARDIOVASCULAR
DISEASE, THEY HAVE MODELS, MOST
MODELS THAT PEOPLE ARE COMING
OUT WITH DON'T JUST LOOK AT SEX
SPECIFIC DIFFERENCES BUT
INCORPORATE GENDER AND
ENVIRONMENTAL FACTORS.
AND I THINK THAT'S WHAT I WANTED
TO SHOW HERE.
NEXT SLIDE PLEASE.
SO THIS IS ABBREVIATED SUMMARY
OF SEX DIFFERENCES IN OSA, THIS
CANNOT BE COMPREHENSIVE TO
EVERYTHING WE KNOW BUT THESE ARE
AREAS I'M INTERESTED IN AND SO
HOPEFULLY YOU'LL FIND THEM
INTERESTING TOO.
IN PREVALENCE, THERE'S A BIG
SPREAD.
THIS PARTICULAR NUMBERS, THESE
NUMBERS CAME FROM A TABLE FROM
2016, PUT TOGETHER A BUNCH OF
PREVALENCE STUDIES TO SHOW
WIDESPREAD IN HOW MUCH WE KNOW
ABOUT PREVALENCE IN MEN AND
WOMEN.
ONE THING ABOUT THAT, IT DOES
NOT TELL US MUCH ABOUT
PREVALENCE LOOKING FOR
MENOPAUSAL STATUS AND AGE,
THINGS LIKE THAT.
STILL RATHER LIMITED.
WE KNOW ABOUT PHYSIOLOGY
DIFFERENCES, IN AIRWAY ANATOMY,
AND RESPIRATORY CONTROL, AND WE
KNOW THAT MEN HAVE MORE
COLLAPSIBLE AIRWAY THAN WOMEN.
IF YOU LOOK AT THE FIGURE DOWN
IN THE LEFT HAND CORNER IN
HEALTHY PEOPLE THERE ARE SOME
SEX DIFFERENCES, IN SLEEP
ARCHITECTURE.
FIND THERE'S AN INTERESTING SEX
DIFFERENCE WHEN IT COMES TO
REM/NON-RECOMMEND, THAT HAS
GREAT APPLICABILITY TO OSA.
YOU CAN SEE TOP TWO LINES
INDICATE REM OSA, MEN AND WOMEN,
AGE ROUGHLY EQUIVALENT.
BOTTOM TWO LINES, SORT OF DOTTED
LINE, MEN AND THEN BELOW THAT IS
WOMEN.
WE CAN SEE DIFFERENCE IN AHI IN
NON-REM OSA VERSUS REM OSA MEN
AND WOMEN HAVE SIMILAR AHI.
I LOOK FORWARD TO LEARNING MORE
ABOUT THAT.
AND THEN ALL THESE STUDIES THAT
I'VE BEEN TALKING ABOUT SO FAR
HAVE BEEN IN SMALLER SETS.
ON THE RIGHT IS BY SANJAY PATEL,
A LARGE STUDY FROM A CPAP
COMPANY, HE LOOKED AT CPAP
ADHERENCE IN MEN, WOMEN, BY AGE
OVER THREE MONTHS.
YOU CAN SEE IN THIS WOMEN LESS
ADHERENT.
IT'S REALLY INTERESTING
DIFFERENCES EXIST.
IT DOESN'T TELL US WHY THEY
EXIST.
THAT'S ONE OF THE CHALLENGES OF
BIG DATA BUT GIVES A DIRECTION
FOR RESEARCH.
NEXT STUDY PLEASE.
NEXT SLIDE PLEASE.
SO ANOTHER SEX DIFFERENCE IS
MENOPAUSAL STATUS.
IT'S WELL KNOWN MENOPAUSAL
STATUS IS ASSOCIATED WITH
INCREASED RISK FOR OSA AND YOU
CAN SEE HERE IN THIS STUDY,
LIGHT BARS WITH POSTMENOPAUSAL,
AND IN ALL CASE POSTMENOPAUSAL
WOMEN HAVE GREATER RATES.
OTHER THING TO POINT OUT THERE,
IF YOU LOOK AT AGE COLUMNS,
DIAGNOSIS OF OSA, RATES BECOME
ALMOST EQUIVALENT TO MEN'S
RATES.
THAT'S IMPORTANT THAT WE SHOULD
CONSIDER MENOPAUSAL STATUS IN
OSA.
NEXT SLIDE PLEASE.
IT'S UNCLEAR IF INCREASE IN
RATES OF OSA IN POSTMENOPAUSAL
IS DUE TO INCREASE IN ESTROGEN
AND MENOPAUSAL TRANSITION, USING
DATA FROM THE STUDY, COMPARED
RATES OF OSA BETWEEN PRE, PERI,
POSTMENOPAUSAL WOMEN.
AND BOTH USED SELF-REPORTED DATA
TO DETERMINE WHETHER OR NOT
WOMEN MENOPAUSAL STATUS.
THEY CONTROLLED FOR AGE,
OBESITY.
BECAUSE IT WASN'T -- IT'S NOT
CLEAR IF WOMEN -- CONTROLLED FOR
AGE AND OBESITY AND WHEN DOING
SO FOUND EACH ADDITIONAL YEAR OF
MENOPAUSE ASSOCIATED WITH
GREATER AHI.
IT IS IMPORTANT TO MAKE SURE WE
INCORPORATE THIS MENOPAUSAL
STATUS.
HOW DO WE DETERMINE USING BIG
DATA?
IT'S DETERMINED BY SELF-REPORT,
EVEN USING AGE AS PROXY.
WE'RE CONSIDERING A BIG DATA
ANALYSIS, SEX DIFFERENCES,
BETWEEN MEN AND WOMEN.
SO, IN THIS STUDY OVER 300,000
FEMALE VETERANS, STRUCTURED
FIELD DATA AND NATURAL LANGUAGE
PROCESSING TO IMPROVE
CLASSIFICATION OF STATUS,
STRUCTURED FIELDS WERE PATIENT
LAST MENSTRUAL PERIOD,
REPRODUCTIVE SURGERIES,
POSTMENOPAUSE DIAGNOSIS, USE OF
BIRTH CONTROL.
YOU'LL SEE ON THE LEFT 27%
CHANCE DETERMINING MENOPAUSAL
STATUS.
WHEN THEY ADDED NATURAL LANGUAGE
PROCESSING, INCREASE OF 55%.
THIS DOES GIVE POTENTIAL FOR
USING EHR DATA TO LOOK AT SEX
DIFFERENCES IN BIG DATA.
NEXT SLIDE PLEASE.
ANOTHER IMPORTANT ADVANTAGE IN
USING BIG DATA IS TO UNDERSTAND
OSA HETEROGENEITY, AND THROUGH
PHENOTYPING.
IN THIS REVIEW, FINDINGS FROM
MULTIPLE CLUSTER ANALYSES
STUDIES, A LOT OF YOU ARE
FAMILIAR WITH THIS.
THIS FIGURE SHOWS OVERARCHING
SUBTYPE WHERE THEY COMBINED
DIFFERENT STUDIES INTO
OVERARCHING SUBTYPE AND BASED ON
DEMOGRAPHICS, CO-MORBIDITIES,
SYMPTOMS.
AND MY GOAL IS NOT TO GO THROUGH
ALL OF THIS PHENO-- YOU KNOW,
ALL OF THIS, ARE WE ON THE RIGHT
SLIDE?
THANK YOU, SORRY, I DIDN'T HAVE
IT IN FRONT OF ME FOR A SECOND.
THIS IS ABOUT PHENOTYPING.
AND SO PHENOTYPING, THE POINT OF
THE SLIDE TO SHOW THAT THERE'S
MEN IN SLIDE A, B, C,D, AND
NO -- ONLY ONE PHENOTYPE WOMEN,
WE HAVE MORE INFORMATION ABOUT
MEN WHEN WE LOOK AT PHENOTYPING
THIS WAY.
NEXT SLIDE PLEASE.
SO NOW I'M GOING TO GIVE AN
EXAMPLE FROM MY OWN RESEARCH,
ANOTHER WAY TO CONSIDER
ASSESSING SEX DIFFERENCES IN OSA
IS THAT WOMEN ARE MORE LIKELY TO
HAVE MILDER THAN MODERATE TO
SEVERE OSA.
WOMEN HAVE BEEN UNDERSTUDIED.
IT'S IMPORTANT THAT WE CONSIDER
LOOKING AT MILD OSA, CONSIDER
OVERALL SEX DIFFERENCES.
USING A COHORT FROM SLEEP HEART
HEALTH STUDY INVESTED SEX
DIFFERENCES AND SUBTYPES, SO
EVERYONE HAD AHI BETWEEN 5 AND
15, SAMPLE BY SEX.
WE DETERMINED SUBTYPES.
WE FOUND FOUR SYMPTOM SUBTYPES
SIMILAR TO PREVIOUSLY WELL KNOWN
AND CITED PAPER BY MY COLLEAGUE
WHO YOU JUST HEARD FROM.
WE DID A STUDY IN THE COHORT,
AND SO WE FOUND SAME SUBTYPES,
EVEN THOUGH I WAS LOOKING AT
MILD HE WAS LOOKING AT MODERATE
TO SEVERE, MINIMAL, DISTURBED
SLEEP.
NEXT SLIDE PLEASE.
MODERATELY AND EXCESSIBLEY
SLEEPY.
DIFFERENCES BY SEX IN DISTURBED
SLEEP, MODERATE, THERE WEREN'T
DIFFERENCES BETWEEN MINIMAL AND
EXCESSIVELY SLEEPY.
NEXT SLIDE PLEASE.
WE ALSO COMPARED SYMPTOM
SUBTYPES WITH MILD OSA TO
MODERATE TO SEVERE, VERY LITTLE
SEX DIFFERENCES BETWEEN THE MEN
AND THE WOMEN IN TERMS OF OSA,
SEVERITY, OSA CATEGORY OF
SEVERITY.
SO THE COLUMNS ON THE LEFT
N--WE ALSO SEE THE RED BOXES
THAT WOMEN SHOWED THEY WERE MORE
FOUND IN THE DISTURBED SUBTYPE
AND THE MEN HAD BOXES THAT WERE
MUCH SMALLER BUT IT'S A MORE
COMPLEX STORY THAN JUST SAYING
THAT WOMEN HAVE MORE DISTURBED
SLEEP THAN MEN, WE ALSO SEE THAT
THE MEN HAD MORE MODERATELY
SLEEPY AND LESS DIFFERENCES IN
THE EXCESSIVELY SLEEPY SUBTYPE.
SO MY POINT HERE IS THAT
ALTHOUGH THROUGH DIRECT
COMPARISONS, WE'RE ABLE TO BE
GAIN MORE INFORMATION WITH
WOMEN, SO THE LAST SLIDE IS WE
DID A BUNCH OF BRAINSTORMING OF
COLLEAGUES AND CONDITIONS TO SEE
THE CHALLENGES THEY FACE WHEN
ASSESSING SEX DIFFERENCES IN BIG
DATA.
AS I SAID YESTERDAY, SUBTLE
DIFFERENCES CAN BE AMPLIFIED.
THE FIRST 1 HERE IS EHR DAT
AHERE WE FOUND THAT--WE TALKED
ALREADY IN THE MENOPAUSE SLIDE
THAT THERE WERE FEW STRUCTURED
FIELDS DATA SPECIFIC TO WOMEN,
SO THAT'S 1 STRUGGLE WITH THE HR
DATA AND THE SECOND 1 I WANT TO
SPEND A MOMENT ON IS THIS IDEA
THAT BALANCED SAMPLES SO WE--WE
MIGHT WELL, A FAIRLY EQUAL
AMOUNT OF MEN AND WOMEN, BUT
THERE IS SOME DATA THAT WOMEN
ARE UNDERDIAGNOSED FOR OSA, AND
SOME OF IT MIGHT BE BECAUSE MORE
WOMEN WERE LIKELY SLEEPING IN
POVERTY EXPE DON'T FIND THEIR
WAY INTO THE HEALTHCARE SYSTEM,
IT COULD BE BECAUSE THERE'S BIAS
AND FAMILY CARE PROVIDERS,
WHATEVER THEY MIGHT NOT BE
PLANNING THEMSELVES INTO THE EHR
DATA TO BE STUDIED SO THERE
MIGHT BE A PROBLEM WITH SORT OF
MISSING WOMEN, AND MIGHT PLAY A
ROLE IN BIAS AND OTHER BIASES
AND ALSO WITH THE EHR DATA THERE
MIGHT BE LACK OF DIFFERENCES
THAT DIFFERENTIALLY IMPACT WOMEN
COMPARED TO MEN.
LIVING IN POVERTY AND LITTLER
ISSUES, ADHERENCE DATA I TALKED
TO A CP A P REP IT THAT THEY'RE
SIMILAR TO PREDICT ADHERENCE
USING THEIR DATA SETS BUT THEY
DON'T HAVE LOOSE GENDER
VARIABLES, CULTURAL VARIABLES
THAT WE COULD USE TO DETERMINE,
YOU KNOW TEASE CO VARIANTS AND
CONFOUNDERS THAT MIGHT AFFECT
ADHERENCE.
HEALTH INSURANCE CLAIMS DATA IS
ANOTHER 1.
THIS IS AN IMPORTANT SOURCE FOR
OSA INVESTIGATIONS, I HAVE
WORKED IN THIS, SOME THINGS TO
KEEP IN MIND HERE IS THAT THE
WORLD ECONOMIC FORUMS, GENDER
GAP REPORT REPORTED THAT AVERAGE
WORKING WOMAN, THE AVERAGE
WORKING WOMAN IN THE U.S. SPENDS
18% MORE ON HEALTHCARE COSTS
THAN MEN.
AND WOMEN ARE MORE LIKELY TO
VISIT MORE HEALTHCARE
PROFESSIONALS MORE OFTEN THAN
MEN SO IN THIS INSTANCE WE MIGHT
HAVE MORE INFORMATION ON WOMEN
THAN MEN.
THERE IS MUCH PROMISE IN THE
BIOMETRICS AND WEARABLE DEVICES
BUT AGAIN WE WILL NEED TO
INCORPORATE SOCIAL VARIABLES,
HEALTH VARIABLES BECAUSE I THINK
IT'S EASY TO FIND SEX DRVESS AND
THEN SORT OF INTERPRET THEM AND
NOT INTERPRET THEM IN A SUBTLE
WAY, AND WE NEED THE ADDITIONAL
INFORMATION.
AND THEN, SOCIAL MEDIA, YOU KNOW
THERE'S ALWAYS BIAS BUT I DO
WORK WITH WITH A COLLEAGUE THAT
USES SOCIAL MEDIA BUT WE DO KNOW
THAT THEY DO HAVE LESS ACCESS TO
SOCIAL MEDIA.
AND THEN RESEARCH REPOSITORIES,
WE DO KNOW THAT THEY'RE NOT
ALWAYS GOING TO HAVE DATA THAT
ARE NOT SPECIFIC TO WOMEN, THEY
MAY NOT BE BALANCED FOR WOMEN,
AND THE LAST THING THAT'S NOT ON
HERE THAT I SHOULD MENTION IS
SURVIVAL BIAS IN LOWNGITUDEINAL
STUDIES AND THOSE WOMEN TEND TO
HAVE A LONGER LIFE SPAN, SO ALL
OF THESE ARE OUR SUBTLE BIAS
THAT WE NEED TO BE AWARE OF WHEN
WE DO THESE STUDIES.
NEXT SLIDE, PLEASE.
>> DOCTOR, YOU HAVE 2 MINUES
LEFT NOKAY, THANK YOU.
SO THE LAST COUPLE SLIDES HERE,
THE OTHER THINGS TO THINK ABOUT
IS THE UP WARD SLEEPINESS SCALE
IS BEING COLLECTED IN THE CLINIC
I WORK WITH ON ALMOST EVERY
SINGLE PATIENT, HOWEVER THERE IS
A FAIR AMOUNT OF DATA THAT IS
NOT AS RELIABLE IN WOMEN, IT'S
MORE RELIABLE IN MEN, MOST OF
THE OSA SCREENING TOOLS, MANY OF
THE OSA SCREENING TOOLS ARE MORE
RELIABLE IN MEN THAN WOMEN AND I
PUBLISHED A STUDY RECENTLY ABOUT
THE--I PUBLISHED A STUDY
RECENTLY THAT WE SHOWED CUT
POINTS FOR THE MAP SCALE BUT
WEEE THIS ALSO IN SOME OF THE
OTHER SCALE SCALES, SO THERE
COULD BE--SO WE HAVE TO BE
CAREFUL WHEN WE USE THEM IN BIG
DATA, WE ALSO KNOW THAT MEN AND
WOMEN MIGHT HAVE DIFFERENT
DEFINITIONS OF SLEEP QUALITY AND
IT GOES WITHOUT SAYING ALL THOSE
INTERSECTIONAL CONSIDERATIONS WE
WANT TO BE SURE WOOER
INCORPORATE, WOMEN ARE NOT A
MONOLITH SO WE NEED TO THINK
ABOUT, BLACK WOMEN, ETHNICITY,
AGE, GENDER IDENTITY, SEXUAL
ORYEBTATION, SWREP CODE,
COMORBID CONDITIONS AND
EVERYTHING LIKE THAT.
NEXT SLIDE, PLEASE SO THERE ARE
FURTHER OPPORTUNITIES FOR
EXPLORATION OF OSA USING BIG
DATA AND AI, I THINK THESE ARE
SOME OF THE IDEAS I HAVE BUT I
HOPE THAT YOU WILL HAVE--MAKE
SURE YOU ALL HAVE DEFINITE
IDEAS, SO WHAT ARE THE RATES OF
OSA DIAGNOSIS OF MEN COMPARED TO
WOMEN ARE WOMEN STILL BEING
UNDERDIAGNOSED, ARE THERE
OUTCOMES FOR METABOLIC DIN
DROAMS OR DIABETES, I WAS
INTERESTED IN HEARING HOW AI
COULD HELP INCORPORATE
DIFFERENCES IN SLEEP
ARCHITECTURE AND ALSO THOSE
BENEFITING FROM CP A P
TREATMENT, AND THEN THE NEXT
SLIDE PLEASE.
SO IN CONCLUSION, A LOT OF WHAT
WE KNOW, A LOT OF KNOWLEDGE WE
HAVE ABOUT WOMEN AND SLEEP HAS
BEEN FOUND IN SMALL DATA SETS
AND THERE'S NOT BEEN A LOT OF
DIVERSITY, SO REALLY THIS
WORKSHOP HAS BEEN GREAT, I
LEARNED ABOUT OPPORTUNITIES THAT
EXIST FOR BIG DATA, AI AND
PROCESSING AND IMPROVING OUR SEX
DIFFERENCES IN OSA,--THERE ARE
LOTS OF NOVEL METHODOLOGY
INCLUDING PROCESSING FOR
MENOPAUSE, THE BIGGEST THING I
JUST WANT TO SAY IS, YOU KNOW
WHEN WE DO OUR STUDIES, WE JUST
NEED TO CONSIDER STRATIFYING BY
SEX AND RECORDING OUR RESULTS
BECAUSE MANY TIMES WOMEN HAVE
DATA SETS BUT WE DON'T FIND OUT
IF THERE WERE DIFFERENCES IN SEX
DIFFERENCES.
SO INCLUDE THOSE DATA SETS WHEN
POSSIBLE AND EVALUATE
QUESTIONNAIRES, AND THE NEXT
SLIDE.
THANK YOU.
THAT'S ALL I HAVE RIGHT NOW.
JUST THAIRNG YOU SO MUCH I WILL
TAKE ANY QUESTIONS.
>> THANKS SO MUCH FOR THE TALK.
GIVEN THE TIME I THINK WE SHOULD
GO INTO THE Q&A, SO WHY DON'T WE
OPEN THE FLOOR TO ALL OF OUR
SPEAKERS IN THIS SESSION.
>> I HAVE NO QUESTIONS IN THE
Q&A, SO IF ANYBODY WANTS TO ASK
ANY NOW, THERE'S A WELCOME BUT
WE HAVEN'T GOTTEN ANY PRIOR TO
THIS.
>> I GUESS 1 QUESTION IS, 1
THING I WOULD LIKE TO KNOW IS,
WELL IN GENERAL I WOULD LIKE TO
GET A LITTLE CROSS TALK GOING
BETWEEN THESE--AMONG THESE TALKS
WHICH ARE GENERALLY EXCELLENT
BUY-IN WHAT DO--BUT WHAT DO YOU
THINK, MAYBE START WITH
DR. MORRIS AND DR. SPIRA, ARE
THERE SEX OR GENDER VARIABLES
THAT WERE NOT THINKING OF TAKING
INTO,A --ACCOUNT WHEN SAY
PREDICTING THE INCIDENCE OF
ALZHEIMER'S DISEASE FROM SLEEP
DATA.
IS THERE--IS THERE SOME MORE
NUANCE WE NEED TO BRING INTO THE
CONVERSATION?
>> SO, I'M HAPPY TO JUMP IN AND
JUST SAY THAT I THINK THAT
SOMETHING THAT I'VE BEEN VERY
INTERESTED IN IS WE HAVE A SEX
DIFFERENCE IN DEMENTIA
PREVALENCE AND WOMEN ARE MORE
LIKELY TO HAVE DEMENTIA, AND I
KNOW--I THINK JONAH WORKS WITH
REBECCA THURSTON IN THIS AREA,
INCLUDING THE ALZHEIMER'S
DISEASE AREA WITH RESPECT
TO--AND THERE ARE ACTUALLY--SO,
EVERYONE ON THIS CALL HAS A LINK
TO PEOPLE WHO ARE DOING A LOT OF
WORK ON SEX DIFFERENCES AND
SLEEP, I KNOW FIONA BAKER WITH
SSRI HAS BEEN DEEPLY INVOLVED IN
THAT BUT I'VE ALWAYS BEEN VERY
CURIOUS ABOUT THE EXTENT TO
WHICH SEX DIFFERENCES AND SLEEP
IS ATTRIBUTABLE TO SO MANY
FACTORS INCLUDING CHILD REARING,
PREGNANCY, FAMILY CARE GIVING,
MENOPAUSE, ALL OF THESE THINGS
MIGHT ADD UP TO CONTRIBUTE TO A
GREATER RISK FOR DEMENTIA.
AND NOT TO MENTION--DID I
MENTION [INDISCERNIBLE], AND WE
KNOW SLEEP CHANGES ACROSS THE
MENSTRUAL CYCLE.
SO I THINK THERE ARE THINGS TO
CONSIDER, THERE'S A LOT TO SAY
THERE, AND I KNOW MUCH MORE
ABOUT THE SLEEP AND DEMENTIA
SIDE THAN THE SLEEP AND SEX AND
GENDER SIDE SO I WILL STOP
TALKING.
>> YEAH, IF I CAN JUMP IN, I
THINK--I ACTUALLY TALKED TO
FIONA BAKER BEFORE I GAVE THIS
TALK BUT I REALLY WISH SHE
TALKED ABOUT A STUDY THAT WASN'T
QUITE READY BUT I WOULD HAVE
LOVE TO PRESENT IT WHICH WAS IN
BIG DATA, BUT, THE--I THINK,
TOO, WHEN WE THINK ABOUT THESE
THINGS, THE DIFFERENCES, DO THEY
ADD UP TO WHEN WE THINK ABOUT
EVERYTHING YOU JUST MENTIONED,
DO THEY ADD UP TO ME FINDING IN
MANY, MANY WAYS, JUST WHAT WE
CONSIDER OBSTRUCTIVE SLEEP APNEA
IN SLEEP BECAUSE THERE'S SO MANY
THAT WE HAVE, WE MIGHT HAVE THE
OPPORTUNITY TO THINK ABOUT
REDEFINING HELP SOME OF THESE
DISORDER--THERE'S SO MANY
DIFFERENCES AND HOW WE MEASURE
IT AND EVERYTHING ELSE LIKE
THAT.
SO YEAH, AS FAR AS MEASURES,
WE'RE TRYING TO ACCOUNT FOR AS
MANY AS POSSIBLE ESPECIALLY WHEN
WE'RE THINKING ABOUT WOMEN,
INCLUDING MENOPAUSAL STATUS.
NWELL, LET ME JUST BRIEFLY
INCLUDE AN ADDITIONAL POINT HERE
THAT [INDISCERNIBLE] IN ORDER TO
SEE THAT YOU'RE IN THIS
TRANSITIONAL PERIOD, EXTENDED
PERIOD, BUT A LESSON
[INDISCERNIBLE] SEEMS TO HAVE
QUALITIES FROM BOYS TOO AND WE
DO SEE FROM EARLY AGES THAT
THERE ARE DIFFERENCES AND
ASSOCIATIONS BETWEEN SLEEP
PROBLEMS AND MENTAL HEALTH.
WE HAD A RECENT STUDY WHERE WE
SHOWED THAT AS PART OF THE ABCD
STUDY, THE LESSON FEMALES REPORT
SHORTER SLEEP DURATIONS, LATER
BED TIMES, MORE PROBLEMS FALLING
TO SLEEP AND ALL THESE THINGS
THAT ARE ASSOCIATED WITH
MENSTRUATION ISSUES, SO I THINK
WE HAVE TO START
BROADENING--THESE EXPERIMENTS.
>> YES.
>> YOU HAVE A QUESTION FROM THE
VIDEOCAST AUDIENCE.
HOW CAN PRACTICING CLINICIANS
PARTICIPATE IN A BROAD RATE IN
THESE RESEARCH PROJECTS.
>> SO I CAN ANSWER SOME OF THAT
ANYWAY, I COLLABORATE WITH QUITE
A FEW CLINICIANS IN STUDIES I
DO, I'M A HUGE HUMAN RESEARCHER
SO I HAVE STUDIES, AND I COULD
BT DO IT WITHOUT THE CLIN IPGZS
WHO ARE THE PEOPLE WHO ARE
SEEING THE EMPLOYMENT PROGRAMS
AND CAN PROVIDE ME WITH SOME OF
THE CHALLENGES THAT THEY HAVE
AND WEARING CP A P, AND SOME OF
THE CHALLENGES THEY HAVE
INITIATING AND QUALITATIVELY WHY
THEY HAVE CONTINUED TO WEAR IT.
SO I THINK IT IS IMPORTANT FOR
US WHO ARE SEEING OUR PATIENTS
EVERY DAY.
>> ANYBODY ELSE WANT TO CHIME
IN?
>> IF NOT WE CAN GO TO LARRY WHO
HAS HIS HAND UP?
>> YEAH, SO DR. MORRIS, YOU
SHOWED ON 1 OF YOUR EARLIER
SLIDES THE DIFFERENCES BETWEEN
MEN AND WOMEN AND THE RANGES
WERE HUGE, SO DOES THAT INDICATE
A PROBLEM WITH DIAGNOSIS OR
HETEROGENEITY OR WHAT?
BECAUSE THAT'S HISTORICALLY BEEN
THE DIFFICULT THING TO ESTIMATE,
I KNOW?
THE ESTIMATES HAVE NOT GOTTEN
BETTER OVER TIME.
>> NO, THAT'S ACTUALLY SOME OF
THE WORK THAT I'M ACTUALLY DOING
WITH DIEGO, YEAH, BECAUSE WHEN
WE LOOK AT THE HI AND WE LOOK
THEA THE DIFFERENCE BETWEEN REM
AND NONREM, THE CMS GUIDELINES
WE CAN ONLY LOOK AT AND CONSIDER
SOMEBODY DIAGNOSED WITH AHI, BUT
WITH OSA, WE'RE AT 3 OF 5% OF
THE OX GENERATEDDATION, THE AHI
SHOW IT SHOULD BE 4% OX
GENERATEDDATION SO WE'RE LOSING
A LOT OF WOMEN THAT ARE NOT
DIAGNOSED BECAUSE WE'RE USING
THE CMS GUIDELINES, SO YES, I
THINK THOSE SPREADS ARE
DEFINITELY AS A RESULT, WE
TALKED ABOUT THE MODALITIES,
THEY'RE DEFINITELY A RESULT OF
JUST, MAYBE BE MORE CONSISTENT,
I'M NOT SURE IN HO WE DIAGNOSE
WOMEN AND MEN.
>> YEAH, IT'S A CONTINUING
PROBLEM.
NTHANKS.
>> ALL RIGHT, THANK YOU VERY
MUCH, THANKS FOR THIS BRIEF BUT
ENLIGHTENING DISCUSSION AND
THANKS FOR THOSE GREAT TALKS AND
AT THIS POINT, LET'S GO TO OUR
10 MINUTE BREAK AND WE WILL SEE
EVERYBODY BACK HERE AT 2:55.
THANK YOU.
>> THANKS TO THE SPEAKERS
APPRECIATE YOUR TALKS.
>> THANK YOU.
THANK YOU FOR BEING HERE TODAY
AND I AM VERYY HAPPY TO KICKOFF
OFF SESSION 5 WHICH IS ON THE
FOCUS ON POPULATION AND
ENVIRONMENTAL IPT --INFLUENCES D
ELGTICAL ISSUES.
THE FIRST SPEAKER TODAY THAT WE
HAVE FOR THIS SESSION WILL BE
DR. AZIZI SEIXAS FROM THE
UNIVERSITY OF MIAMI TALKING
ABOUT PRECISION AND PERSONALIZED
POPULATION SLEEP HEGHT: SMART
AND CONNECTIVE SYSTEMS.
DR. SEIXAS IS THE INTERIM AND
INAUGURAL CHAIR FOR THE
DEPARTMENT OF INFORMATICS OF
HEALTH DATA SCIENCE AT THE
MIAMI
SCHOOL OF MEDICINE AND HE'S ALSO
A FUNDED LEADER WITH THE AIM
PROGRAM, WHICH AIMS TO INCREASE
PARTICIPATION AND REPRESENTATION
OF COMMUNITIES THAT ARE
CURRENTLY UNDERREPRESENTED IN AI
AND NL, WITH THAT I WILL HAND
THE STAGE OVER TO DR. ZIZI.
NTHANK YOU EVERYONE, REALLY,
THANK YOU FOR THAT VERY GRACIOUS
INTRODUCE, FIRST OF ALL, YOU
KNOW I AM FEELING VERY GRATEFUL
FOR BEING ASKED TO PRESENT TO
YOU BECAUSE AS I HAVE BEEN
REFLECTING ON THESE LAST 2 DAYS,
I HAVE NOTHING BUT FEELINGS OF
FULLNESS AND GRATITUDE BECAUSE
PREVIOUSLY DR. MICHAEL
[INDISCERNIBLE] WHO HOSTED A
SIMILAR SLEEP 101 TALK WHEN HE
ASKED ME THIS TO PRESENT, YOU
KNOW THERE ARE ABOUT 2 OR 3
SPEAKERS I KNOW TODAY, WE HAVE 2
FULL DAYS WORTH OF SPEAKERS.
SO HUGE KUDOS TO
DR. [INDISCERNIBLE] AND
DR. BROWN AND HER TEAM FOR
INVESTING IN THIS SPACE.
AND AS MUCH AS I AM VERY FULL,
AND GRATEFUL FOR THIS
INVITATION, I ALSO AM AWARE THAT
MYSELF AS WELL AS MY ESTEEMED
PANELISTS ARE THE LAST SPEAKERS
FOR THESE 2 DAYS, AS WELL AS WE
ARE ESSENTIALLY CAUGHT BETWEEN A
ROCK AND A HARD PLACE BECAUSE,
YOU KNOW WE SERVE--YOU KNOW WE
ARE BETWEEN A VERY LONG WEEK AND
HOPEFULLY EXCITING WEEKEND SO I
NOT SURE IF I SHOULD SEE THIS
HAS AN HONOR OR A CHALLENGE, I
WILL SEE IT AS BOTH AND MY HOPE
IS THAT I WILL SHOW YOU WHO I
AM, MY HOPE IS THAT I WILL
INFORM YOU, I WILL AROUSE YOU
AND MOVE YOU BECAUSE I THINK
THIS IS WHAT THIS SESSION CALLS
FOR.
YOU KNOW THE, THE FOCUS ON
POPULATIONS AND ETHICAL ISSUES
ARE NOT AN AFTERTHOUGHT BUT
SHOULD ALWAYS BE CONTEXTUALIZED
BECAUSE AS WE'RE TALKING ABOUT
DATA, DATA I CONSIDER DATA AS
THE RAW MATERIAL AND INFORMATION
WHICH IS MAKING MEANINGFUL
INSIGHTS FROM DATA SHOULD ALWAYS
BE SEEN IN CONTEXT AND SO NEXT
SLIDE, PLEASE.
AND SO 1 OF THE THINGS THAT I
WILL FOCUS ON TODAY, IS THAT I
HOPE I WILL KICK OUR PANEL OFF
BY FOCUSING ON REFLECTIONS AND
ASPIRATIONS IN TERMS OF WHERE WE
WANT TO GO, I WON'T PROVIDE TOO
MUCH, YOU KNOW STATISTICAL
ANALYSIS BECAUSE I REALLY WANT
YOU ALL TO LISTEN WITH YOUR
EYES, AND SEE WITH YOUR EARS AND
I KNOW THAT SEEMS COUNTER
INTUITIVE BUT WHEN YOU LISTEN
WITH YOUR EYES, YOU HAVE A SENSE
OF DIGESTION WHERE YOU
UNDERSTAND THE MEANING AND
IMPORTANCE WHATEVER YOU SEE AND
WHEN YOU LISTEN WITH YOUR EARS,
I WANT YOU TO FEEL MOVED BUT
ALSO MOVED AND COMPELLED TO
SEARCH FOR TRUTH AND JUSTICE
BECAUSE THIS IS NOT JUST ABOUT
SLEEP HEALTH, THIS IS ABOUT
HEALTH IN GENERAL AND HOW WE'VE
GONE ABOUT DO THANKSGIVING IS
THAT WE CREATED AN ENTEAR BODY
OF FRAMEWORK, A BODY OF RESEARCH
THAT LOOKS LIKE PRECISION AND
PERSONALIZED POPULATION ON HEGHT
WHERE WE FOCUS ON DESCRIBING
PHENOMENA, FOCUS ALSO ON
DISCOVERY PARTICULARLY LOOKING
AT WHAT ARE SOME UNIQUE CAUSES
AND ETIOLOGIES OF DISEASE, IN
THIS CASE, SLEEP HEALTH AND THEN
TRY TO UNDERSTAND AND DISCOVER
WHAT THESE SOLUTIONS ARE, WHO
MIGHT THESE SOLUTIONS WORK FOR
AND THEN LASTLY WE FOCUS ON
SOLVING IT, HOW CAN WE PROVIDE
PERSONALIZED AND COMMUNITY BASED
SLEEP INTERVENTIONS FOR ALL.
NEXT SLIDE, PLEASE.
ONE OF THE FRAMEWORKS WE HAVE
LED IN THIS SPACE, MY COLLEAGUES
AND I, YOU KNOW PRIMARILY
DR. [INDISCERNIBLE] WHO IS A
DEAR COLLEAGUE.
WE LEAD THE CENTER FOR SLEEP AND
CIRCADIAN SCIENCES AT UNIVERSITY
OF MIAMI IS THAT WHEN YOU'RE
INTERESTED IN STUDYING
POPULATIONS, WHETHER IT BE
THROUGH THE LENS OF EQUITY OR
DISPARITIES THAT YOU MUST HAVE
THE LASER FOCUSED ON WHAT YOU
FIND AND A WORK FLOW AS TO HOW
TO GET HERE, FIRST YOU MUST
DEFINE THIS DEFINITION PROVIDES
THIS LENS THROUGH WHICH YOU CAN
OBSERVE DISPARITIES OR
INNEREQUITIES, BUT YOU ALSO NEED
TO HAVE A GOOD SENSE OF HOW ARE
YOU GOING TO MEASURE THESE
DIFFERENCES, VERSUS DISPARITIES,
HOW ARE YOU GOING TO MEASURE
EQUITY AND WHAT KIND OF
ANALYTICAL TOOLS ARE NEEDED FOR
THIS?
THEN WE'RE GOING TO NEED TO
FOCUS ON DESCRIPTION EMPLOY DO
WE FOCUS ON THE LIVED
EXPERIENCE, OR DO WE ALSO FOCUS
ON MULTILEVEL DETERMINANT ANDS
CORRELATES AND THE REASON WHY
THIS IS IMPORTANT, SOME OF YOU
WHO HAVE HEARD ME PEEK BEFORE,
THAT I BELIEVE AND HAVE ALWAYS
SAID THAT SLEEP AND PARTICULARLY
SLEEP HEALTH IS THE
QUINTESSENTIAL OMNIPHENOMENON
WHERE IT CAN BE SEEN AS A CAUSE
OF RISK OR DETERMINANT, A
PREDICTER AND AN ARTIFACTOR OF
CONSEQUENCE, SO WE NEED THOSE
LENS TO BETTER UNDERSTAND, TO
SEE WHAT ARE WE ACTUALLY SEEING
AND FROM THERE WE WILL EXPLAIN
AND DEFINE MECHANISMS AND THEN
WE WILL SOLVE TO FIND PLAUSIBLE
AND STRATEGIC UNSCALABLE
SOLUTIONS.
NEXT SLIDE, PLEASE.
THE VISION OF PRECISION AND
PERSONALIZED POPULATION HEALTH
HAS AT ITSELF CORE ANALYTICAL
TOOLS BUT ALSO PRECISION
EXPOETIC SOMMIC RESEARCH WHERE
YOU'RE FOCUSING ON PRECISION
EXPOSOMES THAT LEAD TO PRECISION
MEDICINE BECAUSE OFTEN TIMES AND
WE TALK ABOUT PRECISION
MEDICINE, WE ARE TRYING TO BE
PRECISE TO 1 INDIVIDUAL.
BUT WHAT WE HAVE LED OVER THE
LAST DECADE OR SO IS A NEW WORK
FLOW IS A NEW FORM OF ANALYTICAL
METHODOLOGIES THAT INCLUDES
TRADITIONAL STATISTICS AS WELL
AS ADVANCED MATHEMATICAL
MODELING AS WELL AS SIMULATION
MODELING WHERE WE CAN LEARN FROM
THE POPULATION TO INFORM THE
INDIVIDUAL AND THEN LEARNING
FROM INDIVIDUALS TO INFORM OUR
UNDERSTANDING OF A GROUP OF
INDIVIDUALS WITHIN A POPULATION
WILL BE CALLED PROFILES AND ONCE
YOU'VE UNDERSTOOD WHAT THESE
CLUSTER PROFILES ARE AND WHO
THOSE ARE, THEN WE CAN CREATE AN
ITERATIVE REFINEMENT AND
UNDERSTANDING OF AN INDIVIDUAL.
NEXT SLIDE, PLEASE.
AND SO I JUST WANTED TO SHARE
WITH YOU, 1 OF THE STUDIES THAT
WE HAVE CURRENTLY UNDERWAY.
BECAUSE WE BELIEVE THAT IN THE
PRESENCE AND THE NEED TO WEAR
THESE NEW THEORETICAL AND METHOD
LOGICAL LENS TO UNDERSTAND HELG
DISPARITIES AT POPULATION
LEVELS, THAT THERE IS A VERY
CLEAR AREA THAT WE HAVEN'T DONE
AS MUCH WORK.
SO I RECOGNIZE THE AUDIENCE WE
HAVE, PARTICULARLY MY PEERS BUT
ALSO THE NIH BECAUSE WHAT I WILL
SHOW YOU WHAT WE'VE DONE THIS
FAR, WE JUST DON'T WANT TO BE
THE ONLY 1S DO THIS, WE WANT TO
ENCOURAGE THE NIH TO PROVIDE US
MANY RESOURCES TO BE ABLE TO
UNDERSTAND URBACTIVITIES AND
PROJECTS AND RURAL DIVIDES.
AND SO NEXT SLIDE, PLEASE.
WE CREATED THE DORMIR STUDY
BECAUSE WE RECOGNIZE THAT THE
RURAL AND URBAN HEALTH DIVIDE
AND VERY STARK.
WHEN YOU LOOK AT HEALTH RELATED
QUALITY OF LIFE TO HEALTHCARE
ACCESS AND USE TO CHRONIC HEALTH
CONDITIONS AND HEALTH BEHAVIORS,
AND WHEN YOU STRATIFY A CROSS
RACIAL ETHNIC GROUPS THAT YOU
SLEEP APNEA AND OBESITYY THAT
YOU'RE--SEE THAT THERE ARE STARK
CONFERENCES AND IF YOU WERE TO
CONTRAST THAT WITH AN URBAN
AREA, THAT YOU WILL SEE
VARIABILITY NOT JUST BETWEEN
RURAL AND URBAN AREAS BUT ACROSS
RACIAL ETHNIC COUNTERPARTS IN
RURAL AND URBAN AREAS.
SO THIS IS AN AREA THAT IS RIPE
FOR EXPLORATION.
NEXT SLIDE, PLEASE.
WHEN WE DREW DOWN ON THIS, THAT
THE CDC HAS CLEARLY HIGHLIGHTED
AND HAS BEEN DOING SO FOR QUITE
SOMETIME FOR SLEEP DURATION,
THAT WHEN YOU LOOK AT THE AREAS
THAT HAVE THE HIGHEST AGE
ADJUSTED PREVALENCE OF SHORT
SLEEP, THAT WHEN YOU ARE TO
TRACK WHERE THESE ARE THAT THESE
ARE IN TRADITIONAL RURAL AREAS
AND URBAN AREAS AND THE FIGURE
TO THE RIGHT, WHEN YOU DRILL
DOWN MORE AT THE COUNTY LEVEL
THAT THERE ARE POCKETS,
PARTICULARLY RURAL PIECE OF
CONTENTYS AND URBAN POCKETS
TYPICALLY OFTEN TIMES, THE
COMMONALITY HAVE TO DO WITH
SOCIOECONOMIC STATUS OR SOCIAL
DETERMINANTS OF HEALTH, BUT WE
HAVE CREATED A FRAMEWORK CALLED
SPEDO, SOCIAL BEHAVIORIAL AND
POLITICAL AND ENVIRONMENTAL
DETERMINANTS OF HEALTH WHICH WE
BELIEVE ACTUALLY DRIVE MANY OF
THE COMMONALITIES ON DIFFERENCES
WE'VE SEEN IN URBAN AND RURAL
AREAS EMPLOY NEXT SLIDE, PLEASE.
WHEN YOU DRILL DOWN, NOT JUST
BEYOND GOING BEYOND SLEEP
DURATION, WE CAN ALSO SEE IT IN
TERMS OF SLEEP DISORDER
SEQUENCES WHERE PARTICULARLY
INDIVIDUALS EXPRESSING THAT THEY
HAVE, YOU KNOW INSOMNIA LIKE
SYMPTOMS WHEN YOU CUT ACROSS THE
DEFINITE AREAS THAT INDIVIDUAL
WHO IS LIVE IN NONURBAN AREAS OR
WHAT WE CALL NONMETROAREAS HOW
ABOUT REPORT HIGHER LEVELS OF
ISSUES FALLING ASLEEP AS WELL AS
STAYING ASLEEP AND SO, THESE ARE
SOME CRITICAL AREAS THAT WE NEED
TO BE AWARE OF, NEXT SLIDE,
PLEASE.
SO WHAT MIGHT BE DRIVING, WHAT
MIGHT BE SOME COMMONALITIES
BETWEEN URBAN AND RURAL AREAS,
OF COURSE, I DON'T WANT YOU ALL
TO THINK THAT I AM SAYING THAT
EVERY URBAN OR EVERY RURAL AREA
IS A MONOLITH BUT HERE ARE SOME
GBL FEATURES THAT IF YOU ARE
TRYING TO GET INTO THIS LINE OF
WORK, LOOKING AT DIFFERENT
POPPULESS, PARTICULARLY PLACE
BASED POPULATIONS THAT YOU MUST
LOOK AT WHAT WE CALL THE GENERAL
DESCRIPTIONS OF THIS THAT AREA,
WHAT IS THE POPULATION LIKE AND
WHAT ARE THE LIFESTYLES, WHAT'S
THE LEARNING LIKE, WHAT'S THE
MOBILITY LIKE, WHAT'S THE
ECONOMIC ACTIFORTS, WHAT IS SOME
ENVIRONMENTAL IMPACT THAT MIGHT
BE DRIVING THESE DISPARATE
OUTCOMES WHARKS IS SLEEP AND
TIMING LIKE, ISSUES AROUND
SOCIAL JETLAG THAT SOME OF MY
OTHER COLLEAGUES DISCUSSED
EARLIER, WHAT ARE THE DIFFERENT
LIGHT EXPOSURES AND THEN WHEN
ARE THE DIFFERENT SLEEP QUALITY
AND HEALTH IMP LIAISON KAIGS.
NEXT SLIDE, PLEASE.
NOW IF YOU WERE TO FRAME THIS
INTO AN ECOLOGICAL MODEL, IN
TERMS OF TRYING TO UNDERSTAND
WHAT MIGHT BE DIFFERENT AND WHAT
MIGHT BE SIMILAR CREASES URBAN
AND RURAL AREAS, THAT WE HAVE
LAID OUT THE BIOLOGICAL AND
CLINICAL, VIERMAL, PSYCHOSOCIAL
AND SOCIAL DETERMINANTS THAT
MIGHT SHED LIGHT AS TO WHY IS IT
THAT WE'RE SEEING SOME OF THESE
DIFFERENCES BETWEEN URBAN AND
RURAL AREAS.
I AM ONLY HIGHLIGHTING THIS NOT
TO BE EXHAUSTED BUT I'M ONLY
HIGHLIGHTING THIS TO SHOW THE
BREDTH AND COMPLEXITY OF THIS
ISSUE AND MY HOPE IS THAT IT
WILL MOVE YOU TO CONSIDER OTHER
UNIQUE AND NOVEL IBD'RE
INDICATOR ANDS DETERMINANTS IN
YOUR OWN RESEARCH AS WELL AS
KROWR CLINICAL PRACTICE.
SO IF YOU'RE LOOKING AT
BEHAVIORIAL FACTORS, LOOKING AT
URBAN AREAS, HIGH LEVEL OF
CAFFEINE USE AND TECHNOLOGY USE,
CONTRASTING THAT WITH RURAL
AREAS THAT THERE HAS BEEN A
LOWER CAFFEINE USE AND
TECHNOLOGY USE AND THAT'S WELL
EVIDENCED.
SIMILARLY IN TERMS OF
ENVIRONMENTAL FACTORS,
URBACTIVITIES AND PROJECTS
AREAS, GREATER LIGHT AND NOISE
POLLUTION, AS WELL AS TRAFFIC
AND INDUSTRY, SIGNIFICANT URBAN
HEAT ISLANDS, YOU KNOW THAT
DISRUPT SLEEP COMFORT WHEN YOU
CONTRAST THAT WITH RURAL AREAS
THAT IS TYPICALLY QUIET, DARKER,
COOLER AREAS, NATURAL SETTINGS
THAT MAY BE MORE CONDUCIVE, NOW
I AM NOT JUXTAPOSING THE 2
DIAMETRICALLY OPPOSITE TO EACH
OTHER PER SE, BUT THIS IS WHERE
OUR STUDY IS TRYING TO
UNDERSTAND WHERE THERE ARE
COMMONALITIES, WHERE THERE ARE
DIFFERENCES AND DO THOSE
COMMONALITIES OR DIFFERENCES
PREDICT HEALTH OUTCOMES,
PARTICULARLY SLEEP HEALTH
OUTCOMES AND POTENTIAL DOWN
STREAM HEALTH CONQUENCES SUCH AS
CARDIO METABOLIC HEALTH, SUCH AS
CARDIOVASCULAR DISEASE, SUCH AS
AGING AND DEMENTIA AND THE LIKE,
NEXT SLIDE, PLEASE.
NOW IF YOU ARE INTERESTED IN
DOING THIS TYPE OF WORK, HOW DO
YOU DO THIS, IT WAS KIND OF HARD
FOR ME TO KIND OF CATEGORIZE
THIS FOR YOU, BUT 1 OF THE
THINGS THAT I WANT PEOPLE TO
REALIZE AND FOR THOSE WHO DON'T,
YOU KNOW, DOING COMMUNITY WORK
OR POPULATION WORK, THE MOST
IMPORTANT THING IS SETTING UP
YOUR INFRASTRUCTURE.
NEXT SLIDE, PLEASE.
SO LET'S FOCUS ON WHAT KIND OF
ADMINISTRATIVE STRUCTURES YOU
WILL NEED, FIRST YOU HAVE TO
ENGAGE THE COMMUNITY AND I WILL
SHARE IT IN TERMS OF HOW WE'VE
DONE THIS FOR RURAL AREAS AND
IT'S NOT VERY DISSIMILAR TO HOW
WE'VE DONE THIS IN RURAL AREAS
AS WELL.
HOW ARE YOU GOING TO ROLL OUT
SCREENING AND RECRUITING PEOPLE
AND SERVING PEOPLE CONSECUTIVELY
IN HOW ARE YOU GOING TO ENGAGE
PEOPLE TO PROVIDE A WIDE VARIETY
OF CLINICAL DATA ON BEHAVIORIAL
OUTCOMES IN AND THEN HOW CAN YOU
ACTUALLY ELEVATE YOUR LEVEL OF
ANALYSIS WHICH IS NOT JUST
FOCUSED ON THE INDIVIDUAL BUT
YOU CAN LOOK AT WHETHER IT BE
FAMILY BASED, COMMUNITY BASED,
COUNTY BASED, REGION BASED
BECAUSE LAYERING DOORS, UNITS OF
ANALYSIS WILL PROVIDE DEEPER,
RICHER INSIGHTS FOR US TO BETTER
UNDERSTAND WHAT MIGHT BE DRIVING
POPULATION HEALTH DIFFERENCES
AND HOW WE CAN GO ABOUT CHANGING
IT WHETHER THROUGH INTERVENTIONS
OR WHETHER IT BE THROUGH POLICY
NEXT SLIDE, PLEASE.
SO LOOT ME SHOW YOU HOW WE'VE
DONE THIS SO WE HAVE CREATED A
HUB AND SPOKE MODEL ABOUT HOW WE
GO ABOUT RECRUITING PEOPLE AND
SETTING UP AN ADMINISTRATIVE
STRUCTURE IN RURAL AREAS, SO
WE'RE CURRENTLY IN SOUTH MR.
SPEAKER FLOOR AND WHAT WE'VE
GONE ABOUT TO DO IS THAT WE
CREATED A HOOK AND THERE'S
OPERATIONAL DEVELOPMENTAL
ENDOCRINOLOGY MISSION AND I
WON'T GET BO WRITING THEM
BECAUSE I KNOW WE'RE RUNNING
AGAINST TIME BUT HA WE SET UP
THE HUB, THE AREA THAT MOST
COMMUNITY MEMBERS IN THIS CASE
WERE FOCUSED ON LATINO ANDS
URBAN AND RURAL AREAS AND WE TRY
TO FIND A HUB AS TO WHERE THEY
CAN SERVE AS THE RESOURCE FOR
THAT COMMUNITY AND BE AICIAL TO
HELP US WITH RECRUITMENT BUT
ALSO THEY'RE THE CONDUITS OF THE
COMMUNITY AND THESE ARE OFTEN
TIMES TRUSTED MESSENGERS BUT
TRUSTED ORGANIZATIONS WITHIN THE
COMMUNITY.
WE HAVE TEAMED UP WITH OTHER
ISSUES WHAT WE CALL SPOKES, LIKE
A QUEST, LIKE A CVS, LIKE THE
LOCAL HIGH SCHOOL, LIKE THE
REGIONAL MEDICAL, LIKE THE
LIBRARIES, LIKE THE WALGREEN'S
IT'S IMPORTANT TO HAVE THE HUB
AND SPOKE MODEL TO SHOW THE
COMMUNITY AS WELL AS A SET OF
ROBUST INFRASTRUCTURE TO
INCREASE RECRUITMENT AND
RETENTION.
NEXT SLIDE, PLEASE.
SIMILARLY WE'VE DONE THIS IN
ANOTHER COMMUNITY, A RURAL
COMMUNITY BUT IT'S VERY
DIFFERENT AS YOU CAN SEE.
IN THIS CASE, THAT THE HUB
ITSELF IS A CATHOLIC CHURCH, YOU
KNOW THIS COMMUNITY IS
TRADITIONALLY CATHOLIC LATINAS
AND WE HAVE FORMED A SPOKE
AROUND THIS HUB AND NOW
WE'RE--WE'VE ENLISTED THE
COLLEGE OF LOOR FLOOR KEYS,
AGAIN QUEST DIAGNOSTICS AND CVS,
AND A FREE CLINIC IN FLOWER
FLOOR AS WELL AS DIFFERENT
HEALTH SYSTEMS AS WELL AS
SUPERMARKETS.
NEXT SLIDE, PLEASE.
YOU CAN SKIP THROUGH AGAIN,
PLEASE.
SO NOW I WILL SHARE WITH YOU,
HOW CAN WE GO ABOUT ESTABLISHING
THIS ADMIN TRAITIVE
INFRASTRUCTURE, I JUST SHARED
WITH YOU, HOW IS IT YOU'RE GOING
TO RECRUIT PEOPLE, AND I WILL
TALK ABOUT HOW IS IT THAT WE CAN
ROLL OUT YOU KNOW VERY
COMPLICATED AS WELL AS SIMPLE
STUDIES AND SO WHAT WE'VE DONE,
WE'VE CREATED ADDITIONAL HEALTH
EQUITY MODEL WITH MY COLLEAGUE
DR. JUDY LONG AND OTHERS WHERE
WE WANT TO FOCUS ON FIRST, YOU
HAVE TO TRAIN THE STAFF WHAT TO
DO AND THEN YOU FOCUS ON
ADAPTATION OF STUDY RECALL
MATERIALS AND THEN FOCUS ON
MODIFICATION AND WE CREATE A
COMMUNITY STEERING COMMITTEE
WHICH IS A DIFFERENT THING FROM
AN ADVISORY BOARD.
NEXT SLIDE, PLEASE.
ADDITIONAL HEALTH EQUITY AND
INCLUSION MODEL FOCUSES ON
TRYING TO IDENTIFY BARRIERS, AS
WELL AS FINDING DIGITAL
SOLUTIONS TO OVERCOME THOSE AS
WELL, NEXT SLIDE, PLEASE.
ONE OF THE THINGS WE FOCUS ON
AND WE BELIEVE THAT IN
ADDITIONAL SOLUTIONS, THE
PANACEA THAT CAN KILL ALL ILLS
IN THE SIGNIFICANT BOLT NECKS
AND BARRIERS IN TRANSLATIONAL
RESEARCH TRADITIONAL RESEARCH,
BUT WE THINK IT'S A REALLY,
REALLY NICE CREATIVE STRATEGY,
IT HELPS US WITH ENGAGEMENT, IT
HELPS US WITH RECRUITMENT, AND
KREENING AND CONSENT THELPS US
WITH ADMINISTRATION OF SERVICE
AS WELL AS STUDY EXECUTION AND
DISSEMINATION, NEXT, SLIDE,
PLEASE.
WHAT WE HAVE DONE IS THAT WE
HAVE CREATED OUR IN-HOME HOME
GROWN MONITORING CALLED THE MAIL
CONTROLLER OF BANK WHEREBY WE
HAVE PUT ESSENTIALLY, NOT
PHYSICALLY IN THE BOX BECAUSE
WEB CONNECTED RECOGNIZE IN URBAN
AND RURAL AREAS IN PARTICULAR
THAT PEOPLE MAY NOT HAVE THE
TRANSPORTATION RESOURCES TO COME
TO HEALTH ASSISTANCE SO WE FILL
UP THE ENTIRE RESEARCH PROCESS
BY PUTTING THE PARTICIPANT IN
THE CENTER OF THE RESEARCH
EXPERIENCE AND SO WE ANY TO
THEM.
SO THIS IS A HIGHLY
SOPHISTICATED AND COMPLICATED,
YOU KNOW MONITORING SOLUTION AND
THUS FAR, WE HAVE DISTRIBUTED
OVER 700 ACROSS 3 OF OUR NIH
STUDIES, SO THIS HAS BEEN A VERY
SUCCESSFUL IN THAT WE'RE
COLLECTING SLEEP ACTIVITY, WE
SEND PEOPLE'S HOMES A MART
PHONE, WE COLLECT MONITORS, WE
SEND OUT AN AIR QUALITY DEVICE
THAT MEASURES AIR QUALITY AND
HUMIDITY, WE MONITOR BLOOD
PRESSURE, WE HAVE A SLEEP
MEASURE THAT MEASURES CARDIO
PULMONARY AS WELL AS A SMART
SCALE AND ALL THAT GETS INGESTED
AND WE PROVIDE THAT JUST WALKING
IN THROUGH THE ENTIRE PROCESS,
SO IT'S NOT TOO MUCH OF A BURDEN
AS WELL AS WHEN WE'RE COLLECTING
BIOSPECIMEN DATA, WE ACTUALLY,
YOU KNOW FLIP TO THAT.
SO WHAT WE HAVE DONE IS WE
PILOTED USING SOME VERY NOVEL
DRY BLOOD SPOTS, SAMPLING AS TO
HOW WE WILL DO THAT, I THINK
THAT WE WILL PRESENT THAT AT OUR
SLEEP MEETING BUT WE PARTNERED
WITH QUEST DIAGNOSTICS AS WELL
AS LOCAL FLEBOT ME CUTCHES WHERE
WE TAKE ALL THE BARRIERS OUT SO
THE POINT I'M TRYING TO DRIVE
HOME IS THAT WHEN YOU'RE DOING
RESEARCH IN THESE SPACES, YOU
WANT TO INCREASE YOUR CHANCES OF
COLLECTING COMPLETE FULL QUALITY
DATA AND THESE ARE SOME OTHER
STRATEGIES WE'VE UTILIZED TO
INSURE THAT WE'VE GOTTEN THOSE.
NEXCUSE ME, DOCTOR, HAVE YOU 2
MINUTES LEFT.
>> PERFECT.
ONE AREA THAT WE TRY TO WORK ON
AS WELL IS WAKIND OF DATA AND
TOOLS, YOU KNOW IF YOU'RE
INTERESTED IN USING DIGITAL
SOLUTIONS, TO DO THIS TYPE OF
EXPOETICSOME WORK, YOU WANT TO
MAKE SURE YOU HAVE THE NECESSARY
DATA, INFRASTRUCTURE TO DO SO,
NEXT SLIDE, PLEASE.
NEXT SLIDE, PLEASE, THIS WAS
SAYING THE SAME THING IN TERMS
OF HOW YOU WANT TO DO IT, THIS
IS FOCUSED ON HOW WE GO ABOUT
IT, 1 THINK THIS I WANT TO SHOW
OR TALK ABOUT BREFLY IS THAT
WHEN WE TALK ABOUT URBAN AND
RURAL RESEARCH STUDIES, OFTEN
TIMES THESE TAKE ON
EPI-FRAMEWORK AND I AM NOT
SAYING ANYTHING IS WRONG WITH
THAT BUT WE DO HAVE A WONDERFUL
OPPORTUNITY TO ROLL OUT
INTERVENTIONS TO SEE PLACE BASED
TYPES OF EFFECTS ON HOW
SUCCESSFUL CLINICAL TRIALS ARE.
WE HAVE LED SEVERAL DIGITAL
SOLUTIONS, BY USING, YOU KNOW,
DIGITAL SOLUTIONS TO DO THAT AND
WE ROLL THIS OUT AND HAVE BEEN
EXTREMELY SUCCESSFUL, NEXT
SLIDE, PLEASE.
WE USED PEER HEALTH EDUCATORS TO
SUPPORT US DURING THIS IN BOTH
URBAN AS WELL AS RURAL AREAS.
NEXT SLIDE, PLEASE.
YOU COULD ADVANCE PLEASE, NEXT
SLIDE.
ONE OF THE THINGS YOU WANT TO BE
AWARE OF IS THAT WHEN YOU'RE
LOOKING AT ADHERENCE, THAT YOU
WANT TO TAKE INTO FULL
CONSIDERATION, SOCIAL
DETERMINANTS OF HEALTH, NEXT
SLIDE, PLEASE.
SOCIAL DETERMINANTS OF HEALTH
SPAN MANY DIFFERENT CATEGORIES
AND WE TAKE THOSE INTO
CONSIDERATION.
NEXT SLIDE ISSUE PLEASE AND WHAT
WE HAVE DONE IS THAT WE HAVE A
CLINICAL TRIAL THANKS TO THE NIA
WHERE WE'RE LOOKING AT WAYS IN
WHICH WE CAN OPTIMIZE SLEEP
APNEA ADHERENCE IN INSTANCES
WHERE WE WANT TO BE ABLE TO
CAPTURE SOCIAL DETERMINANTS OF
HEALTH AS WELL AS PROVIDING CARE
NAVIGATION TO ADDRESS SOME OF
THE SIGNIFICANT BARRIERS THAT
COULD IMPAC SOMEONE'S ADHERENCE
TO SLEEP APNEA.
NEXT SLIDE, PLEASE.
>> SO I'LL FINISH HERE.
ONE OF THE THINGS I LOOK TO
LEAVE YOU ALL WITH IS THAT WE
HAVE A REALLY GREAT OPPORTUNITY,
THIS IS NOT JUST ABOUT LOOKING
AT SLEEP AND BIG DATA, THIS IS
REALLY SITUATING SLEEP AS BIG
DATA TO INFLUENCE AND INFORM
OVERALL HEALTH.
SO THE PROGRAM WE LAID OUT HERE
IN MIAMI, IF YOU'RE MOVING FROM
RIGHT TO LEFT, WE BUILT THE MAIL
BOX AND OUR GOAL IS TO CREATE
DIGITAL TWINS OF INDIVIDUALS AS
YOU CAN SEE WITH THE MONITORING
SOLUTION, THEN FROM THERE WE
ALSO ARE CREATING SMART MEMORY
CLONE TECHNOLOGIES BECAUSE WE
BELIEVE IN AMBIENT TECHNOLOGY,
THAT'S THE WAY FORWARD.
IT'S NOT JUST LOOKING AT
TRADITIONAL MEASUREMENTS OR
COLLECTING SLEEP DATA BUT HOW
OTHER FORMS OF DATA THROUGH
AMBIENT DATA COLLECTION AND WE
WANT TO ALSO INCLUDE SMART CITY
COMPONENTS BECAUSE INDIVIDUALS
ARE NESTED IN HOMES AND HOMES
ARE NESTED IN NEIGHBORHOODS AND
THIS I BELIEVE WILL ALLOW US TO
MOVE BEYOND INDIVIDUAL BASED
MOTEL MONITORING SOLUTION AS YOU
SEE IN THE MAIL BOX TO A
COMMUNITY POPULATION BASED AND
THAT'S THE PROGRAM THAT WE CALL
MILEAGE, SO WITH THAT THANK YOU
SO MUCH AND I AM LOOKING FORWARD
TO HEARING THE OTHER SPEAKERS
AND HEARING YOUR WONDERFUL
QUESTIONS.
THANK YOU FOR THE OPPORTUNITY.
>> THANK YOU SO MUCH FOR THAT
WONDERFUL TALK.
IN THE INTEREST OF TIME WE WILL
MOVE TO OUR NEXT SPEAKER AND
SAVE ALL THE QUESTIONS FOR THE
END.
PLEASE PUT YOUR QUESTIONS IN THE
CHAT SO THAT WE CAN OR Q&A SO
THAT WE CAN CAPTURE THOSE EMPLOY
SO OUR NEXT SPEAKER IS
DR. MARIANTHI-ANNA,
KIOUMOURTZOGLOU, AND I AM SORRY
IF I MISPRONOUNCED THAT WHICH I
AM SURE I DID.
SHE IS AT COLUMBIA UNIVERSITY,
SHE WILL PRESENT ON BIG
ENVIRONMENTAL DATA FOR SLEEP
CIRCADIAN RHYTHM RESEARCH.
HE IS AN ENVIRONMENTAL ENGINEER
IN EPIDEMIOLOGIST AND CURRENTLY
ASSOCIATE PROFESSOR FOR
ENVIRONMENTAL SOCIAL SCIENCES AT
COLUMBIA, WHERE SHE RESEARCHES
STATISTICAL ISSUES RELATED TO
ENVIRONMENTAL EPIDEMIOLOGY.
I WILL HAND THE FLOOR OVER TO
YOU MARIANTHI-KNOWA ANA, AND YOU
HAVE A GREAT SESSION.
NTHANK YOU DOCTOR, AND YOU SAID
MY LAST NAME GREAT, THAT WAS
PERFECT.
THANK YOU.
SO IT'S GREAT THAT I FOLLOW
DR. SEIXAS, AND WHAT I WILL SAY
HERE IS VERY COMPLIMENTARY TO
THE TOOLS THAT HE DRIEBED, SO
THIS IS NOT THAT WAY OR THIS WAY
OF GETTING TO DATA.
IDEALLY WE WOULD LIKE TO DO
BOTH.
SO NEXT SLIDE, PLEASE.
SLEEP, WE ALL HEAR KNOW THAT
BECAUSE OF RESEARCH AND PERSONAL
EXPERIENCE IS VERY IMPORTANT FOR
PHYSICAL AND MENTAL COMELGT WELL
BEING AND ENVIRONMENTAL
EXPOSURES CAN INFLUENCE OUR
SLEEP QUALITY SO THE--MAYBE
OBVIOUS 1S OR MORE OBVIOUS ARE
TEMPERATURE AND NOISE, OF COURSE
THEY CAN DISRUPT SLEEP BUT
THERE'S INCREASING EVIDENCE FOR
THE ROLE OF LIGHT AND AIR
POLLUTION AND POTENTIALLY OTHERS
DIRECT AND INDIRECT PATHWAYS.
SO ACCURATELY ASSESSING
ENVIRONMENTAL EXPOSE IRBS AND
LINKING THOSE TO SLEEP AND
CIRCADIAN RHYTHM DATA IS KEY TO
UNDERSTANDING THEIR ROLE IN
SLEEP HEALTH.
AND CAVEAT THAT I WILL NOT
DISKESES OR EXPOSE BIOMARKERS
DURING THIS TALK, I WILL NOT
TALK ABOUT EXPOETICSOMICS,
DR. SEIXAS DID AN AMAZING WITH
THAT BUT INSTEAD I WILL DISCUSS
SLEEP QUALITY AND ENVIRONMENTAL
EXPOSURES LINKING SLEEP AND
CIRCADIAN RHYTHM EMPLOY NEXT
SLIDE, PLEASE.
I KEEP TRYING TO DO IT MYSELF.
SO CONSIDERATIONS FOR LINKING
DATA, FIRST OF ALL, I AM NOT
PROVIDING HERE SOURCES.
IF YOU HAVE ANY QUESTIONS ABOUT
SPECIFIC DATA SOURCES OR
SPECIFIC AMBIENT EXPOSURES
DEFINITELY PLEASE LET ME KNOW,
CERTAINLY--CERTAINLY MAIL ME AND
I'M HAPPY TO PROVIDE THOSE, BUT
CONSIDERATIONS FOR LINKAGE ARE
FIRST OF ALL SPATIAL AND
TEMPERRAL RESOLUTION OF THE
ENVIRONMENTAL DATA SO FOR
EXAMPLE WITH WE TALK ABOUT
TEMPORALITY, ARE WE INTERESTED
IN THE ANNUAL AVERAGE, SOMETHING
THAT DIFFERENT CHANGE OR TIME
BUT VERY, VERY SLOWLY CHANGES
OVER TIME OR DO WE WANT
SOMETHING ASSESSED DAILY AND
THEN ALSO, THE SPATIAL
RESOLUTION, WELL, LET ME GIVE
EXAMPLES, FOR EXAMPLE, SOMETHING
THAT DIFFERENT REALLY CHANGE
THAT QUICKLY, IS OUR BUILT
ENVIRONMENT, OF COURSE IT
CHANGES BUT NOT DRASTICALLY
OVERTIME, THAT TAKES YEARS SO
WHEREAS IF QUIEWR THINKING ABOUT
TEMPERATURE OR MAYBE EVEN AIR
POLLUTION THEN MAYBE WE'RE MORE
INTERESTED IN A DAY-TO-DAY
VARIABILITY.
AND THE SPATIAL RESOLUTION IS
ALSO THE AVAILABILITY OF THE
DATA BUT THAT FOR EXAMPLE, IS
DATA AVAILABLE AT THE COUNTY
LEVEL OR VERY HIGH SPATIAL
RESOLUTION FOR EXAMPLE, LIKE 30
SQUARE METER GRIDS, AND FOR SOME
EXPOSURES, COUNTY DATA MAY BE
OKAY, THE EXPOSURE'S QUITE
HOMODPEENIOUS IN SPACE, CERTAIN
AIR POLLUTANTS NOT THAT MANY,
TEMPERATURES MOSTLY HOMOGENIUS
SO IT WOULD BE ACCEPTABLE AT
COUNTY LEVEL.
IF WE'RE INTERESTED IN A VERY
SPECIFIC URBAN ISLAND EFFECT, WE
OBVIOUSLY WANT SOMETHING THAT IS
MORE FINELY RESOLVED THAN COUNTY
LEVEL.
THE RELEVANT TIMES OF ECPOSURE
IS ALSO IMPORTANT FOR THE
SPECIFIC OUTCOME OF INTEREST, SO
THAT'S NOT ONLY THE AVAILABILITY
OF THE DATA, BUT ALSO WHAT WE
ARE INTERESTED IN, SO IS THE
STUDY ABOUT ACUTE SLEEP
DISTURBANCES, IN WHICH CASE, YOU
KNOW URBAN, THE BUILT
ENVIRONMENT AROUND US, WON'T BE
REEL KEEPSAKES VABT FOR THAT,
VERSUS CHRONIC SLEEP QUALITY
ISSUES.
SO THE LAST THING IN THIS SLIDE
IS THE ADDRESS, WHAT LOCAL
INFORMATION, WHAT RESIDENTIAL
INFORMATION DO I HAVE IN THE
STUDY PARTICIPANTS IN DO I HAVE
SOME GEOGRAPHY RELATED TO THEM
ON WHICH I CAN ASSIGN THE DATA
BECAUSE IF I DO NOT KNOW
ANYTHING, ABOUT WHERE THEY ARE,
IF I ONLY KNOW STATE FOR
EXAMPLE, THAT'S NOT VERY HELPFUL
FOR ENVIRONMENTAL DATA, IF I
KNOW COUNTY OR CITY, VERSUS NOT
SOME BUT OTHERS, IF I HAVE
ADDRESS, I HAVE ACCESS TO ALL
TYPES OF DATA AND THEN I CAN
EITHER USE AGGREGATE OR ASSIGN
AT THE EXACT ADDRESS EXPOSURES,
NEXT SLIDE, PLEASE.
OH AND IF THESE ARE TOO VAGUE,
WHAT I'M SAYING BECAUSE I'M NOT
GIVING SPECIFIC EXAMPLES, PLEASE
LET ME KNOW IN THE QUESTIONS AND
I CAN ADDRESS IN THE Q&A PART.
OTHER CONSIDERATIONS ARE
PROTECTIONS OF IDENTIFIABLE
DATA, EVEN IF WE LINK PEOPLE SAY
ON THEIR ZIP CODE AND THEN WE
COMPLETELY TAKE THE ZIP CODES
AFTER THE LINKAGE OUT OF THE
FILE, ONCE WE LINK PEOPLE TO
PUBLICLY AVAILABLE ENVIRONMENTAL
DATA, THAT MEANS THAT IT'S
POSSIBLE THAT OTHERS MAY
IDENTIFY OUR STUDY SUBJEKS.
THIS IS SOMETHING THAT MIGHT NOT
BE SUPER CLEAR, SO IT'S VERY
IMPORTANT TO THINK THAT NOW THE
DATA, EVEN IF THE LIFE LONG ARE
STRIPPED FROM THE RECORDS BEING
BE IDENTIFIABLE SO WE NEED EXTRA
PROTECTION, WE NEED TO HAVE THE
SET UP FOR THAT, COMPUTATIONAL
SCALABILITY, EVEN IF MY COHORT
IS ONLY 100 PEOPLE AND I WANT TO
YOU KNOW SIGN TO THEM ANNUAL
EXPOSURES OF FINE PARTICLE THIS,
IS AN AIR POLLUTANT, THE DATA
SETS TO DOWNLOAD, TO GET THAT
WILL PROBABLY BE GIGA BITES OR
TERRA BYTES, SO DO I HAVE THE
CACA PASQUANTINOITY TO DEAL WITH
THOSE DATA OR DO I HAVE
COLLABORATORS THAT CAN HELP ME
WITH THAT.
CONSULT WITH EX PERS ON
EXPOSURES.
I AM ON THE EXPOSURE SIDE, MY
EXPERTISE AND VERY OFTEN I
COLLABORATE WITH EX PERS ON THE
OUTCOME SIDE AND VERY OFTEN I
SEE THAT, YOU KNOW EBPOSURES ARE
JUST NUMBERS.
BUT TALKING WITH EXPOSURE
EXPERTS CAN HELP US UNDERSTAND
THE NUANCES, THE DIFFERENT
ERRORS, DIFFERENT POTENTIAL
CONFOUNDERS, OR PREDICTERS, OR
YOU KNOW ALL THESE THINGS THAT
COULD HELP US WHICHEVER OUR
ANALYSIS IS, WHETHER IT'S
EPIDEMIOLOGIC OR PREDICTION
MODEL, AND THEN FINALLY, IT'S
IMPORTANT TO KEEP IN MIND THAT
AMBIENT LEVELS ARE NOT PERSONAL
EXPOSURES SO WHAT DR. SEIXAS
PRESENTED EARLIER, THOSE GET
CLOSER TO PERSONAL SOLUTIONS OF
EXPOSURE AND SOUND FOR EXAMPLE,
HOWEVER, THAT IS NOT ASSESSING
EXPOSURES THAT WAY, IT'S BETTER
BUT IT'S NOT REALISTIC IF WE
WANT, YOU KNOW, IF WE DON'T HAVE
THE FUNDS, OR IF WE HAVE
3 MILLION SUBJECTS BECAUSE THIS
IS ADMINISTRATIVE DATA SET OR
EVEN A FEW THOUSAND SUBJEKS, SO
THINKING THE POTENTIAL OF
EXPOSURE MEASUREMENT OR AT
UNCERTAINTY AND THE IMPACTS IT
MAY HAVE ON THE MAGNITUDE AND
DIRECTION OF BIAS IS IMPORTANT,
IT'S ALSO IMPORTANT TO CONSIDER
WHETHER THE ERROR MAY BE
CORRELATED WITH THE OUTCOME IN
WHICH IT'S DIFFERENTIAL.
NEXT SLIDE, PLEASE.
SO I WILL TALK ABOUT AIR
POLLUTION SPECIFICALLY HERE
WHICH IS UBIQUITOUS EXPOSURE, WE
ALL BREATHE, WE ARE ALL EXPOSED
TO AIR POLLUTION EVERY MINUTE OF
OUR LIVES AND AIR POLLUTION HAS
BEEN LINKED TO NUMEROUS ADVERSE
HEALTH OUTCOMES ACROSS THE LIFE
SPAN FROM PREGNANCY AND BIRTH
OUTCOMES TO MOOD DISORDERS,
RESPIRATION AND MORTALITY,
NEURODEGENERATION, ALL CAUSE
MORTALITY, IT'S A ERROR LONG
LIST, MANY OF THESE ARE
CAUSEALLY RELATED, THE AIR
POLLUTION HAS CAUSAL EFFECTS ON
THESE OUTCOMES.
NEXT SLIDE, PLEASE.
NOW, INDIVIDUAL RISKS IN AIR
POLLUTION STUDIES ARE NOT
PARTICULARLY HIGH SO FOR
EXAMPLE, IF YOU SEE ISSUES OR
OTHER ISSUES FOR AIR POLLUTION
ON CERTAIN OUTCOMES AND THEN YOU
COMPARE THOSE WITH LET'S SAY
SMOKING, THEY'RE MUCH, MUCH MORE
MODEST AND SMALLER, HOWEVER, THE
IMPLICATIONS FOR POPULATION
HEALTH ARE VERY, VERY HIGH
BECAUSE AS I SAID EARLIER, WE
ALL BREATHE.
SO EACH IF OUR INDIVIDUAL RISKS
ARE LOW, AT THE POPULATION
LEVEL, THE NUMBERS CAN BE LARGE,
AND FOR THIS REASON, IN 2019,
THAT'S THE MOST RECENT GBT,
EXPOSE TOWER AIR POLLUTION WAS
REPORTED TO HAVE RESULT IN
7 MILLION PREMATURE DEATHS PER
YEAR, SO AIR POLLUTION IS
CURRENTLY THE LEADING
ENVIRONMENTAL RISK FACTOR FOR
DISABILITY AJUSTED LIFE YEARS
AND THIS IS THE FOURTH RISK
FACTOR OVERALL, AND THIS IS
LIKELY AN UNDERESTIMATE ISSUES
BECAUSE THIS DID NOT INCLUDE ALL
OUTCOMES THAT ARE BEEN LINKED TO
AIR POLLUTION, SLEEP QUALITY IS
1 OF THEM.
SO THERE HAVE BEEN ONLY A FEW
STUDIES, NOT THAT MANY LOOKING
AT AIR POLLUTION AND SLEEP
QUALITY.
THE RESULTS ARE NEGATIVE MEANING
THAT AIR POLLUTION IS ASSOCIATED
WITH WORSE SLEEP QUALITY AND THE
MECHANISMS, THE HYPOTHESIZED
MECHANISMS MAY INVOLVE
NEUROCHEMICAL AND/OR
PHYSIOLOGICAL EFFECTS.
NEXT, PLEASE.
SO, AS I SAID EARLIER FOR LARGE
POPULATIONS, PERSONAL
MEASUREMENTS ARE EXPENSIVE AND
NOT VERY FEASIBLE ESPECIALLY IF
WE WANT PEOPLE OVER EXTENDED
PERIOD.
IF WE'RE INTERESTED IN LET'S SAY
CHRONIC IMPACTS OR LONG-TERM
EXPOSE TOWER AIR POLLUTION AND
CHRONIC IMPACT IN SLEEP HEALTH,
WE WOULD LIKE TO KNOW PEOPLE'S
EXPOSURES FOR 1 OR 2 YEARS.
IT'S NOT EASY TO HAVE PERSONAL
MEASUREMENTS FOR THIS LONG.
FOR MAYBE IT'S OKAY FOR A
HUNDRED, 300 SUBRECYCLINGS,
MUCH, MUCH HARDER FOR BUDGETS
BUT ALSO LOGISTICALLY TO DO THIS
FOR DOZENS OF THOUSANDS OF
PEOPLE.
SO THE DEVELOPMENT OF AND THIS
HAS LED TO THE DEVELOPMENT OF
ADVANCED TEMPERRAL MODELS TO
PREDICT CONCENTRATIONS AT
PARTICIPANTS ADDRESSES AND THIS
IS JUST 1 EXAMPLE OF VERY
COMMONLY USED MODEL THAT WAS
DEVELOPED BY DISPEAZ ET EALL,
THAT IS THE HARVARD GROUP BUT
HAD
THERE ARE CURRENTLY MULTIPLE
MODELS EXISTING, BUT THEY DON'T
GIVE THE SAME INFORMATION AT
EVERY DAY AND EVERY POINT
LOCATION, NEXT, PLEASE.
MY MY GROUP AND I DEVELOPED
SOMETHING CALLED THE BIOMETRIC
ENSEMBLE, THAT INTEGRATES
INFORMATION ACROSS TEMPERRAL
MODELS AND IF WE DO THAT, THE
DI ET ALL IS 1 EXISTING MODEL,
WE HAVE 7 OR 8 OF THEM
CURRENTLY, BY TEMPERRAL,
PREDICTIVE ACCURACY, HOW
ACCURATE IS THE MODEL AT AIM
TIME AND AT EACH POINT IN SPACE
AND TIME.
SO WE'RE ESTIMATING SPATIAL
TEMPERRAL WEIGHT ANDS THEN WE
ARE ENSEMBLING ALL OF THESE TO
CREATE 1 SURFACE, WELL, DAILY
SURFACE AND IMPORTANTLY BECAUSE
WE DO THIS [INDISCERNIBLE]
FRAMEWORK, THIS PROVIDES
TEMPERRAL UNCERTAINTY OF THIS
PREDICTION, SO WE DON'T ONLY GET
THE PREDICTIVE CONCENTRATION
WHICH IS WHAT ALL OTHERS MODELS
PROVIDE BUT ALSO WE GET FOR EACH
CONCENTRATION, IT'S UNSSHT AND
THAT UNCERTAINTY SHOWS THE
PREDICTED UPCERTAINTY AT THAT
LOCATION AND TIME BUT ALSO
THE--INCORPORATES THE
DISAGREEMENT BETWEEN EXISTING
MODELS SO IF WE'RE INTERESTED IN
VARIANCES THAT'S IMPORTANT.
NEXT SLIDE, PLEASE.
YES, SO WHEN WE STARTED THIS, WE
ACTUALLY THOUGHT THAT OUR MODEL
WOULD YIELD THE BEST PREDICTIVE
ACCURACY, IT DOES NOT, I WILL
SAY THAT, BUT WHAT IT DOES IS WE
LOOKED AT THE PREDICTIVE
ACCURACYS BY REGION OF ALL THE
AIDS INPUT MODELS AND EVEN
THOUGH OUR MODEL WAS NOT THE
BEST AT EVERY REGION, IT WAS
CONSISTENTLY IN THE TOP 3 OF THE
8 MODELS IN EVERY REGION, NO
OTHER MODEL DID THAT, SO, WE
HAVE A CONSISTENT EXPOSURE
MEASUREMENT ACROSS THE UNITED
STATES, SO BRINGING IT BACK TO
SLEEP QUALITY, IF I WANT TO
COMPARE THE ASSOCIATION BETWEEN
AIR POLLUTION AMONG PEOPLE IN
LOS ANGELES AND IN NEW YORK, IF
I FIND DIFFERENCES THAT AFFECT
ESTIMATES, NOW THIS IS NOT
BECAUSE OF DIFFERENT EXPOSURE
ERROR BY REGION THIS, IS BECAUSE
AIR POLLUTION MAY HAVE A
DIFFERENT COMPOSITION OR OTHER
DIFFERENT FACTORS IN THE 2
POPULATION BUT NOT BECAUSE OF
ERROR WHICH IS GREAT BECAUSE
IT'S 1 THING OFF THE LIST.
UNCERTAINTY ESTIMATION IS VERY
IMPORTANT BECAUSE THIS CAN HELP
US WITH VALID INFERENCES OF
HEALTH EFFECT ESTIMATE ANDS THE
LAST 2 ARE NOT NECESSARILY THAT
RELEVANT FOR THIS PANEL, THIS 1
SHOP BUT THE EVALUATION OF
SPECIFIC MODEL WEIGHTS CAN LEAD
TO IMPROVEMENT OF EACH BASE
MODEL AND THEN, THE
IDENTIFICATION OF HIGHEST
UNCERTAINTY LOCATIONS CAN HELP
EFFICIENT PLACING OF NEW
MONITORING STATIONS.
NEXT, PLEASE.
O THIS IS JUST AN ANNUAL AVERAGE
OF 2010 FOR FINE PARTICLES, AND
FINE PARTICLES ARE THESE TINY
LITTLE PARTICLES IN THE AIR,
THEY ARE LESS THAN
2.5-MICROMETERS WE CANNOT SEE
THEM WITH THE NAKED EYE BECAUSE
OF THEIR SIZE, THEY CAN
PENETRATE DEEP BO OUR LUNGS AND
IF THAT'S WHERE THEY CAN CAUSE
INITIALLY SOME LOCAL
INFLAMMATION AND LOCALOX CAITIFF
STRESS AND THEN GO SYSTEMIC AND
PROVIDE IT CAN IMPACT ALL OUR
ORGANS AND ALL OUR SYSTEMS.
SO ON THE LEFT-HAND SIDE, THIS
IS THE ANNUAL AVERAGE FOR 2010
CONCENTRATION OF PM2.5.
AND ON THE LEFT-HAND SIDE,
THAT'S THE NOVELTY IS THAT THIS
IS THE UNCERTAINTY THAT WE HAVE
IN THESE PREDICTIONS SO AS YOU
CAN SEE IN THIS MAP, THERE ARE
CERTAIN AREAS THAT FOR WHICH WE
DO--WE'RE NOT VERY CONFIDENT IN
THE PREDICTIVE CONCENTRATIONS.
NEXT SLIDE, PLEASE.
NOW WHY THIS BECOMES IMPORTANT.
SO STUDIES HAVE CONSISTENTLY
REPORTED HIGHER CONCENTRATIONS
IN LOW INCOME AND MARGINALIZED
COMMUNITIES AND AGAIN DR. SEIXAS
ALREADY DISCUSSED THIS IN THE
PREVIOUS PRESENTATIONS, WELL I
DON'T KNOW ABOUT SPECIFICALLY
2.5 THAT THESE DISPARITIES EXIST
IN THE HOALGT OUTCOME AS WELL.
SO HOWEVER THE MONITOR PLACEMENT
IS NOT RANDOM IN SPACE.
HOW DOES UNCERTAINTY VARY ACROSS
THE SES GRAYIENT IN THIS HAS
IMPLICATIONS FOR INTERPRETING
THE RESULTS, RIGHT IN THE
CONCENTRATIONS, SO HOW DO, NOW
ONLY HOW DID UNCERTAINTY IN THE
EXPOSURE VARIES BUT HOW DO
CONCENTRATIONS VARY AFTER
ACCOUNTING FOR VARIATIONS IN
UNCERTAINTY.
NEXT, PLEASE.
SO, THESE ARE BUSY PLOTS, I WILL
DRIEB THEM IN JUST A SECOND, I
WOULD JUST LIKE TO SAY THAT
THESE ARE PRELIMINARY DATA,
THESE ARE DRIEWBTED AND I KNOW
THIS IS BEING RECORDED BUT JUST
IF YOU SEE THIS IN THE PUBLISHED
PAPER IN A FEW MONTHS, IT MIGHT
LOOK A LITTLE DIFFERENT
DEPENDING ON WHAT THE REVIEWERS
ASK US TO DO.
SO HERE WE'RE COMPARING, THIS IS
THE CENSUS TRACK LEVEL
PROPORTION OF BLACK RESIDENTS ON
THE LEFT, AND Y-ON THE AXIS--ON
THE X-AXIS, I'M SORRY, ON THE
Y-AXIS IS THE UNCERTAINTY OF PF2
PBT 5 CONCENTRATION AND PLEASE
NOTE THAT RIGHT NOW, THE AXES
ARE IN DIFFERENT SCALE, SO THIS
GOES UP TO .6 SO THEY'RE NOT
EXACTLY COMPARABLE HOWEVER WHAT
IS VERY CLEAR IS THAT WITH
INCREASING PROPORTION AT PLOKS
AND REGISTERING THE CENSUS
TRACK, THE UNCERTAINTY ALSO
INCREASES WHEREAS FOR WIDE
RESIDENT ANDS FOR TRACKS WITH
WHITE RESIDENTS, PROPORTION OF
WHITE RESIDENTS ISSUES THE
UNCERTAINTY IS HIGHEST WHEN THE
WHITE RESIDENTS ARE LOWEST.
SO IT PICKS UP AGAIN YOU GO IT'S
NOT QUITE THE SAME.
SO WITH THESE RESULTS WE WERE
QUITE WORRIED DOES THIS MEAN NOW
THAT EVERYBODY HAS CONSISTENTLY
REPORTED ALL COMMUNITIES OF
CENSUS TRACKS WITH HIGHER
PROPORTIONS OF PLAQUE RESIDENT
VS EXPERIENCING HIGHER
CONCENTRATIONS WILL THE RESULTS
CHANGE ONCE YOU TAKE INTO
ACCOUNT THIS UNCERTAINTY.
NEXT SLIDE.
TURNS OUT NO, THESE ARE THE
RESULTS TAKING THE ASSOCIATIONS
BETWEEN PROPORTION OF PLAQUEOT
LEFT AND WHITE ON THE RIGHT,
RESIDENTS WITH THE CONITRATION
OF FINE PARTICLES NOW WHILE
TAKING INTO ACCOUNT THAT
UNCERTAINTY.
SO IT'S VERY, VERY CLEAR THAT
EVEN IF WE TAKE THAT DIFFERENCE
INTO ACCOUNT, IN UNCERTAINTIES,
TRACKS WITH HIGHER PROPORTIONS
OF BLACK RESIDENTS ARE
SIGNIFICANTLY EXPERIENCING
HIGHER LEVELS OF THIS VERY TOXIC
AIR POLLUTANT COMPARED TO ON THE
OTHER HAND, THE MORE THE HIGHER
PROPORTION OF THIS HAS, THE MUCH
STRIKINGLY LOWER AIR POLLUTION
CONCENTRATIONS, THE TRACK
EXPERIENCES, NEXT PLEASE.
SO IN CONCLUSION, PUB LIKELY
AVAILABLE DATA CAN BE VALUABLE
FOR LINKING CERTAIN
ENVIRONMENTAL EXPOSURES,
SPECIFICALLY ENVIRONMENTAL
EXPOSURES TO SLEEP AND CIRCADIAN
RHYTHM RESEARCH, THERE ARE
CONSIDERATIONS FOR LINKAGE AND
ANALYSIS WITH THIS DATA THAT
NEED TO BE TAKEN INTO ACCOUNT
AND ENVIRONMENTAL JUSTICE IS
KEY, IS COMPLEX, IT'S PLULTY
DIMENSIONAL, AND WE CANNOT
IGNORE IT IF OUR GOAL IS TO
EQUITABLY IMPROVE SLEEP HEALTH,
NEXT SLIDE, PLEASE.
AND YES, I WOULD JUST LIKE TO
ACKNOWLEDGE MY AMAZING GROUP
BECAUSE IT'S THEM THAT DO ALL
THE WORK, IT'S NOT ME, I'M YOU
JUST HERE TO PRESENT THE AMAZING
WORK THAT THEY'RE DOING.
SO THANK YOU VERY MUCH.
THAT'S IT.
>> THAT DOUBLE MUTE IS GOING TO
KILL ME TODAY, I HAVE MY CELL
PHONE GOING BECAUSE NIGH NETWORK
IS UNSTABLE SO I DON'T WANT TO
MISS ANYTHING.
THANK YOU SO MUCH FOR THE GREAT
TALK.
IF YOU DON'T MIND, WE WILL HOLD
QUESTIONS TO THE VERY END AND
HOPEFULLY WE WILL BE ABLE TO GET
TO ALL THOSE AND EVERYBODY CAN
ACTUALLY PUT YOUR QUESTIONS INTO
THE CHAT AND WE WILL MOVE TO OUR
NEXT SPEAKER WHO IS DR. JOHN
BRIDGES FROM OHIO STATE
UNIVERSITY THAT WILL TALK ABOUT
THE ETHICAL, LEGAL AND SOCIAL
EMPLICATIONS OF ARTIFICIAL
INTELLIGENCE IN MEDICINE.
SO DR. BRIDGES, IS A PROFESSOR
IN THE DEPARTMENT OF BIOMEDICAL
INFORMATICS AT THE OHIO STATE
UNIVERSITY COLLEGE OF MEDICINE.
HE'S ALSO AN ECONOMIST BY
TRAINING, AND DEVOTES HIS CAREER
TO ADVANCING THE SCIENCE OF
PATIENT ENGAINMENT AND HIS
RESEARCH USING THEORY DRIVEN
SURVEY PUTTING PHOSPHORYLATED
UNDERSTAND THE PREFERENCES AND
PRIORITIES OF PATIENT ANDS OTHER
STAKEHOLDERS IN MEDICINE.
I WILL LEAVE THE FLOOR TO YOU
JOHN, THANK YOU VERY MUCH.
NTHANK YOU VERY MUCH, DANA,
COULD I HAVE THE NEXT SLIDE.
I WANT TO ACKNOWLEDGE SUPPORT
FROM NCATS AND FROM THE
[INDISCERNIBLE] FUNDS, THERE'S
NO OTHER CONFLICTS I NEED TO
DECLARE.
NEXT SLIDE.
SO DURING THE 2 DAY MEETING
WE'VE HEARD A NUMBER OF USES OF
ARTIFICIAL INTELIENCE AND
MACHINE LEARNING, YOU KNOW
AROUND THE COLLEGE OF MEDICINE
AND OTHER PLACES THERE, IS A
DOMINANCE OF DISCUSSION OF THE
APPLICATIONS OF THESE METHODS IN
MEDICINE AND HEALTHCARE.
A LOT OF THE FOCUS HAS BEEN ON
THE TYPES OF METHODDINGS OR THE
CATEGORIES OF METHODS AND
ADVANCING ALGORITHMS, WE HEARD A
FEW OF THOSE TODAY AND NOVEL
APPLICATIONS OF THAT, BUT, 1 OF
THE KIND OF KEY QUESTIONS THAT I
HAVE BEEN MEDITATING ABOUT IS
BROADER DISCUSSIONS AND
OCCASIONS OF THE USE OF
ARTIFICIAL INTELIENCE AND
MACHINE LEARNING IN PRACTICE IN
SOCIETY, ARE LAGGING, WE HEARD
SOME OF COMPONENTS OF THAT AND
THAT'S 1 OF THE MOTIVATIONS FOR
THE WORK THAT I'VE BEEN DOING
AND THE PRESENTATION I'VE PUT
TOGETHER.
THE NEXT SLIDE.
SO FROM MY POINT OF VIEW,
THERE'S A RANGE OF FACES AND AI
USED PROCESSING A LARGE AMOUNTS
OF DATA, DIAGNOSIS OF DISEASE
OVER THE LAST COUPLE DAYS OTHERS
OTHER FORMS OF PREDICTIVE
ANALYTICS, INCREASINGLY AND
PERSONALIZED MEDICINE AND IN
MANY RESPECTS, WITH THE GOAL OF
PROMOTING PATIENT EMPOWER XMENT
1 OF THE MOST INTERESTING AND
CONCERNING APPLICATIONS OF AI
THAT I'VE SEEN IN THE LITERATURE
IS ACTUALLY THE PREDICTION OF
PATIENT PREFERENCES, TRYING TO
USE THIS TO UNDERSTAND WHAT THE
PATIENT WOULD WANT AND THAT
THERE'S INTERESTING APPLICATIONS
WHERE THAT MIGHT BE NECESSARY
BUT IT ALSO POSES SOME
INTERESTING ETHICAL QUESTIONS.
NEXT SLIDE.
SO 1 OF THE ARGUE AMS THAT I'M
GOING TO MAKE IS MUCH LIKE OTHER
PLACES WHERE WE USE LARGE DATA
PARTICULARLY GENETICS AND
GENOMICS AND THEY HAVE AN FIELD
CALLED LC AND WHAT I WOULD LIKE
TO SUGGEST IS THAT LC MEDES TO
BE APPLIED TO OTHER FORMS OF BIG
DATA AND ALSO TO THE APPLICATION
OF ARTIFICIAL INTELIENCE AND
MACHINE LEARNING.
LC STANDS FOR THE ETHICAL LEGAL
AND SOCIAL IMPLICATIONS, IT'S A
TRANSDISCIPLINARY RESEARCH THAT
WAS SPECIFICALLY FOCUSED
INITIALLY ON GENETICS AND
GENOMICS BUT IT'S STARTING TO
FORM TARA LENS LENS LENS LENS
LITERATURES, PARTICULARLY IN
CANCER RESEARCH WHERE WE'RE
SEEING MORE GENETIC ANDS
GENOMICS BUT WE'RE SEEING THE
PRINCIPLES BEING APPLIED FOR
EXAMPLE TO OTHER FORMS OF
COMMUNITY ENGAGEMENT OR CLINICAL
TRIAL PARTICIPATION.
IN OTHER COUNTRIES, THIS IS
CALLED DIFFERENT THINGS, JILLS,
AND INTERNATIONALLY, THIS IS A
COMMENNENT OF A LARGER FIELD OF
HEALTH TECHNOLOGY ASSESSMENT
WHERE HEALTH SYSTEMS AND THE
PUBLIC ENVIRONMENTAL
IMPLICATIONS OF TECHNOLOGIES ARE
CONSIDERED MORE WHOLISTICALLY.
THE NEXT SLIDE.
THERE HAVE BEEN A NUMBER OF ELSI
CONCERNS RAISED BUT WHAT I WOULD
ARGUE IS A NUMBER OF THESE HAVE
JUST FOCUSED IN ON TRADITIONAL
ETHICAL ISSUES SURROUNDING
CONSENT, SAFETY AND
TRANSPARENCY, FAIRNESS, ARE
PARTICULARLY POTENTIAL BIASES
INVOLVED IN ALGORITHMS AND DATA
PRIVACY, WHEN YOU WOONG ABOUT
THE LEGAL ISSUES THERE ARE
SAFETY ASK EFFICACY ISSUES,
LIABILITY, DAILY BASIS THEA
PROTECTION AND SELECTION, AND
SECURITY AND INTELLECTUAL
PROPERTY, THESE ARE SOME
INTERESTING COMPONENTS BUT THEY
HAVE BEEN DISCUSSED IN THIS
CONTEXT.
THE NEXT SLIDE.
SOME OF THE THINGS THAT I THINK
ARE NOT BEING DISCUSSED AS
BROADER, SOCIAL CONCERNS AND
THAT IS, ARE WE ADEQUATELY
ENGAGING AND EMPOWERING THE
PUBLIC IN DISCUSSIONS ABOUT THE
USE OF THESE TECHNOLOGIES.
WHAT ARE OUR MECHANISMS FOR
OVERSIGHT AND SURVEILLANCE IF
THESE TECHNOLOGIES WERE TO BE
USED IN SOCIETY, HOW DO WE
ADDRESS FAIRNESS AND EQUITY IN
THEIR APPLICATION?
WHAT ARE THE ISSUES IN TERMS OF
ECONOMICS, THE COST OF THE
HEALTHCARE SYSTEM, COST TO
PATIENTS AND THE COST TO THE
ECONOMY AND EMPLOYMENT AND THESE
CAN BE BOTH INTENDED AND
UNINTENDED CONSEQUENCES OF THE
USE AND THEN I THINK WE ALSO
HAVE TO THINK MORE
WHOLISTICALLY, ABOUT HOW THE USE
OF THESE TECHNOLOGIES MIGHT
CHANGE HUMAN RELATIONSHIPS.
NEXT SLIDE, WE RECENTLY DID OUR
REVIEW OF EXISTING GUIDELINES
FOR THE DEVELOPMENT AND
APPLICATION OF ARTIFICIAL
INTELLIGENCE AND MACHINE
LEARNING TO THINK ABOUT ALL THE
EXISTING GUIDELINES WHAT ARE THE
DOMINANT ISSUES THAT HAVE BEEN
FOCUSED ON, AND YOU CAN SEE
THERE IN KIND OF LIKE THE DARK
BLUE, IT REALLY IS FOCUSED ON AN
ACADEMIC EXERCISE OF
DEVELOPMENT, VALIDATION AND
REPORTING OF RESULTS AND DURING
THIS TRANSPEARNS AND
DEMONSTRATING THE EFFICACY,
ETHICS IS AN EMERGING ISSUE SO
IS REPRODUCIBILITY, BUT USUALLY
ONLY IN DEVELOPMENT, NOT IN THE
APPLICATION.
THERE IS EMERGING LITERATURE, IN
THE LIGHTER BLUES THERE WITH
REKNARRED TO IMP LEMMATION BUT 1
OF THE AREAS BOTH IN TERMS OF
THE DOMAINS AND INVOLVEMENT IS
THIS ISSUE OF AN ABSENT IN THE
EXISTING GUIDELINES ON ISSUES OF
SURVEILLANCE, IF THESE
TECHNOLOGIES WERE IMPLEMENTED
INTO PRACTICE, WHO WOULD INSURE
THAT THEY CONTINUE TO BE USED
APPROPRIATELY, THOUGH, THEY
DON'T HAVE UNINTENDED
CONSEQUENCES THAT THEY DON'T
CAUSE BIASES, AND WHAT IS THE
ROLE FOR ENGAGEMENT, THE
ENGAGEMENT OF HEALTHCARE
PROVIDERS, HEALTHCARE SYSTEMS,
AND PATIENTS THEMSELVES AND
THEIR FAMILY MEMBERS, SO WE CAN
KIND OF SEE HERE SOME OF THE
ISSUES THAT HAVE KIND OF FOCUSED
IN ON AGENCIES SUCH AS PC ORI,
BUT INCREASINGLY, THE FDA HAS
FOCUSED A LOT ON ENGAGEMENT AND
SURVEILLANCE, 1 WAY THEY'VE DONE
THAT IN THE NEXT SLIDE IS
THROUGH THE 21st CENTURY CURES
ACT, ENIMAIJING THE FDA IN
FOCUSING MORE ON OVERSIGHT AND
PARTICULARLY WITH REGARDS TO THE
USE OF REAL WORLD DATA IN
ASSESSING THE TECHNOLOGY, THINGS
LIKE PROVISIONAL APPROVAL, BUT
THEN ALSO, DICK 8ING 8ING--DICG
TO THE FDA THAT WE NEEDED
GREATER COMMUNITY ENGAGEMENT.
THROUGH THIS AND OTHER
REGULATIONS AT THE FDA, ARE A
WIDE ARRAY OF CONSTITUENCIES,
INCLUDING THE FDA INDUSTRY,
ENGAGING IN THINGS CALLED
PATIENT FOCUS DRUG DEVELOPMENT,
THERE WAS A SPECIFIC TRIAL
MEETINGS WAS ON NARCOLEPSY, AND
THIS ALLOWED THE FDA TO HAVE
DIRECT CONTACT WITH THE
NARCOLEPSY COMMUNITY TO
UNDERSTAND THE DEC FROM THEIR
LIVED EXPERIENCE.
THERE ARE A NUMBER OF OTHER
APPROACHES DONE ON THAT NUMBER
AT THE CDHR AND CBER, THEY WERE
VERY MUCH ENGAGED IN PHOBEUS
GROUPS BUT MEASURING THINGS LIKE
SURVEYS AND THROUGHOUT THE FDA
THERE HAS BEEN A LOT MORE FOCUS
ON THE USE OF PATIENT
PREFERENCES AND PREFERENCE
TRIEWMENTS AND THE ENGAGEMENT OF
RESEARCHERS AND DETERMINING THE
RIGHT OUTCOMES ARE BEING
MEASURED AND THAT PEOPLE HAVE
VIEW POINTS THAT ARE SHARED.
IF WE WERE TO SHARE THIS AS PART
OF THE PATIENT FOCUS DRUG
DEVELOPMENT OR NOW PATIENT
MEDICAL FOCUS MEDICAL TECHNOLOGY
DEVELOPMENT, THE FDA AND OTHERS
NEED TO UNDERSTAND UNMET NEEDS,
THEY HAVE TO ADDRESS OUTCOMES
THAT MATTER, AND THEY HAVE TO
MAKE SURE THAT WE HAVE
ACCEPTABLE TRADE-OFFS, THE FOCUS
OF 21st CENTURY CURES WAS TO
MAKE SURE WE OVERCAME THE VALLEY
OF DEATH WITH REGARDS TO MEDICAL
TECHNOLOGY DEVELOPMENT AND THE
VERY FEW MEDICAL TECHNOLOGIES
THAT ACTUALLY HAVE BENEFIT AND
REAL WORLD PRACTICE.
I WOULD ARGUE THAT MANY OF OUR
BIG DATA APPROACHES AND
APPLICATIONS OF ARTIFICIAL
INTELIENCE AND MACHINE LEARNING
COULD BENEFIT FROM BETTER
INTEGRATION AND UNDERSTANDING OF
THE UNMET NEEDS AND WHAT
OUTCOMES MATTER THE MOST AND
WHAT IS ACCEPTABLE TRADE-OFFS BY
DIRECTLY ENGAGING WITH
CONSTITUENCIES SUCH AS
HEALTHCARE PROVIDERS AND THE
PATIENT, WE HAVE TO COME OUT OF
THE IVORY TOWER TO DO THAT, AND
MEET PEOPLE WHERE THEY ARE TO
UNDERSTAND THEIR NEEDS AND LIVED
REAL WORLD EXPERIENCES.
NEXT SLIDE.
THERE ARE A NUMBER OF WAYS THAT
WE DO THIS IN ELSI RESEARCH.
TRADITIONALLY THIS HAS BEEN DONE
WITH THE USE OF A VIGNETTE STUDY
WHERE A PARTICULAR FRAMEWORK IS
USED, VERY MUCH LIKE A CLINICAL
TRIAL, WE'RE DIFFERING
PERSPECTIVES ARE DONE IN THIS
PAPER, WE--THE VIGNETTE IS KIND
OF CONSIDERING A COUPLE
DIFFERENT CLINICAL APPROACHES,
BUT, THE DEGREE OF ACCEPTANCE
WHEN YOU USE AN AUTOMATIC TOOL,
THE PHYSICIAN USES AN AUTOMATED
TOOL OR PHYSICIAN DOESN'T USE AN
AUTOMATED TOOL, HERE WE CAN
UNDERSTAND THE POTENTIAL
IMPLICATIONS OF THIS BY
UNDERSTANDING HOW THE PUBLIC
MIKE REACT IN THESE DIFFERENT
SITUATIONS.
THE NEXT SLIDE.
THIS COULD BE GENERALIZED TO
CONSIDER MORE FACTORS OR WHAT'S
CONSIDERED A FACTOR VIGNETTE,
THIS IS STARTING TO MOVE WHAT WE
MIGHT CALL A DISCREET CHOICE
EXPERIENCE OR FROM A PREFERENCE
RESEARCH, WE WOULD HAVE A
MULTIPLE VIGNETTES PRACTICES
SENTED TO AN INDIVIDUAL
COMBINING A RANGE OF DIFFERENT
COMBINATIONS OR PROFILES AND
THEN THINKING ABOUT THE
ACCEPTABLE OF USE, BY DOING THIS
TYPE OF SURVEY RESEARCH, WE CAN
NOTE ONLY UNDERSTAND WHAT PEOPLE
VALUE BUT WHAT THEY COULD ACCEPT
IN TERMS OF THE IMPLICATIONS OF
PROVIDING TECHNOLOGY OR
APPROACHES TO DATA ANALYSIS OR
DATA SHARING.
THIS COULD BE EXTENDED THROUGH
THE NEXT SLIDE TO A FULL BLOWN
DISCREET CHOICE EXPERIMENT WHERE
A MEMBER OF DIFFERENT APPROACHES
ARE COMPETING AGAINST 1 ANOTHER,
SO IN THIS WE JUST DON'T SHOW A
SINGLE VIGNETTE, BUT WE SHOW
THROUGH THIS MULTIPLE FACTORIAL
DESIGNS, COMBINATIONS OF
VIGNETTE AND ASK, WOULD YOU
CONSIDER THESE DIFFERENT
OFFERINGS IS NONE OF THESE
POTENTIALLY ACCEPTABLE AND THEN
WE CAN GO IN USING A MORE
COMPLICATED ECONOMETRIC
TECHNIQUES TO UNDERSTAND WHAT
ARE THE DIFFERENT VALUES AND WE
CAN APPLY THIS TYPE OF WORK TO
BOTH PROVIDERS, PATIENTS AND TO
THE GENERAL PUBLIC TO KIND OF
UNDERSTAND WHAT IT IS THAT THEY
VALUE.
THE NEXT SLIDE.
WELL IS 1 OTHER APPROACH THAT IS
INCREASINGLY BEING APPLIED IN
HELT CARE AND IN OTHER AREA ANDS
THAT'S TRYING TO UNDERSTAND WHAT
MATTERS MOST.
IN THIS PAPER, I COLLABORATED
WITH FOLKS AT BAYLOR TO
UNDERSTAND WHAT ETHICAL ISSUES,
RATHER THAN JUST SAYING, WE
BELIEVE THAT THIS ISSUE IS MOST
ETHICALLY IMPORTANT, WE ACTUALLY
PUT AN EXPERIMENT TOGETHER WHERE
WE COMPARED A WHOLE SERIES IN
THIS STUDY, 11 DIFFERENT ETHICAL
CONCERNS THAT HAD BEEN RAISED
ASSOCIATED WITH THE USE OF
CITIZEN SCIENCE, A COULDN'T FIND
AN AI APPLICATION OF THIS, BUT
HERE WE COULD BOTH UNDERSTAND
WHICH OF THESE ARE MOST
IMPORTANT, OR WHICH OF THE
ISSUES ARE MOST IMPORTANT BUT
ALSO LOOK AT LATENT CLASSES OR
TRYING TO FIND OUT NICHES IN THE
COMMUNITY THAT MIGHT FOCUS ON 1
AREA RATHER THAN ANOTHER AND IN
THIS STUDY, WE FOUND 1 GROUP
THAT HAD FOCUSED ON POWER AND
IMBALANCE, AND THE EXPLOITATION
OF PEOPLE AND 1 PEOPLE WHO
REALLY WANTED TO VALIDATE THE
DATA, AND THIS WAS VERY
INDICATIVE OF THE UNDERLYING
CONVERSATIONS THAT IT HAD BUT AT
A DELIBERATATIVE PROCESS MEETING
THAT WAS DISCUSSING THESE ISSUES
BUT USING STATISTICAL TECHNIQUES
WE CAN GENERALIZE THIS AND
UNDERSTAND IN A BROADER
COMMUNITY, BUT ALSO QUANTIFY
THEM IN THE NUMERICAL VALUE SO
RATHER THAN JUST SAYING
ANECDOTALLY WE BELIEVE THIS,
PREFERENCE METHODS AND THESE
TYPES OF VIGNETTE METHODS AND
PRIORITIZATION METHODS ALLOW US
TO COME IN AND USE THIS TO
UNDERSTAND THE BELIEFS AND NEEDS
AND WANTS OF A BROADER
CONSTITUENCY INCLUDING THE
GENERAL PUBLIC.
THE NEXT SLIDE.
SO, THE ARGUMENT IS THAT THERE
IS A MAJOR NEED IN THE
APPLICATION OF AI WHETHER IT BE
TO SLEEP STUDIES OR TO MORE
GENERALLY TO HAVE A DEEPER
UNDERSTANDING OR PROVIDER
PATIENT AND COMMUNITY AND WE
HAVE TO DO THAT THROUGH
ENGAGEMENT.
WE HAVE TO FOCUS ON
IMPLEMENTATION, AND USE IN THE
REAL WORLD RATHER THAN JUST THE
DEVELOPMENT OF THIS, SO WE HAVE
TO MOVE AWAY FROM THE RO-1 TO
THINK ABOUT MORE ABOUT THE
IMPLEMENTATION OF THESE
TECHNOLOGIES IN PRACTICE AND
WHAT THAT WOULD HAVE IN TERMS OF
END USER EXPERIENCE AND PATIENT
EXPERIENCE, WE HAVE TO THINK
ABOUT OUR MEASURES OF
EFFECTIVENESS AND IMPACT, ON
BOTH SYSTEMS AND PATIENT
OUTCOMES.
WE HAVE TO MOVE BEYOND JUST
MAXIMIZING THE AREA UNDER THE
ROC CURVE AND ASK QUESTIONS LIKE
HOW DOES THIS IMPACT TRUST?
HOW DOES THIS IMPACT USE OF
MEDICAL TECHNOLOGIES?
HOW DOES THIS IMPACT THE USE OF
OTHER MEDICAL TECHNOLOGIES?
AND THINKING ABOUT THIS MORE
WHOLISTICALLY.
FOR ME WORK, AREAS OF USING
VIGNETTE STUDIES, PREFERENCE
STUDIES AND PRIORITIZATION
STUDIES PROVIDE 1 VEHICLE THAT
WE CAN APPLY ELSI AND SOCIAL
IMPACT TYPES OF RESEARCH TO
STUDY THE APPLICATION OF AI.
AND I JUST HAVE 1 MORE SLIDE.
IF YOU'RE INTERESTED IN THIS,
I'M VERY INTERESTED IN FORMING
COLLABORATIONS AND APPLYING
THESE TYPES OF METHODOLOGIES TO
LOCK AT NEW AND EMERGING USES OF
DATA AND THE APPLICATION OF AI
AND THE IMPACTS ON SOCIETY AND
PATIENTS, THANK YOU VERY MUCH.
>> THANK YOU SO MUCH DR. BRIDGES
THAT WAS A FANTASTIC TALK.
I WANTED TO GO AHEAD AND KICKOFF
THE Q&A SESSION.
SO IF ALL OF THE SPEAKERS COULD
PUT THEIR CAMERAS BACK ON AND IF
PEOPLE CAN PUT THEIR QUESTIONS
IN CHAT OR RAISE THEIR HAND,
THAT WOULD BE TERRIFIC.
AND I'LL GO ARK HEAD AND START.
THERE WAS 1 QUESTION IN CHAT
ALREADY WHICH I KNOW WAS
ANSWERED BUT I THINK IT'S WORTH
REPEATING FOR THE AUDIENCE
MEMBERS AND VIDEOCAST, AND IT'S
A QUESTION THAT DR. SEIXAS ABOUT
THE LISTS NUMBER OF FACTORS
WHICH LEAD TO MUCH BETTER SLEEP
IN RURAL AREAS, COMPARED TO THE
URBAN AREAS, HOWEVER, WHEN LOOK
BEING AT THE GRAPHS IT LOOKED
LIKE OR THE DATA THAT YOU SHARED
THAT IT LOOKED LIKE THAT
ACTUALLY THAT WAS THE REVERSE,
IT LOOKED LIKE THERE WERE MORE
PROBLEMS IN NONMETROAREAS, CAN
YOU EXPLAIN THIS DISCREPANCY?
>> LET ME JUST BE VERY CLEAR,
--I ANSWERED IT ALREADY BUT
THANKS FOR THE OPPORTUNITY TO
ADDRESS IT, WHAT I SHOWED WAS
THAT AND YOU LOOK AT THE
EPIDEMIOLOGICAL FIGURURES THAT
THERE IS A SCATTERING OF HIGH
PREVALENCE OF SHORT SLEEP BOTH
IN URBAN AND RURAL AREAS, AND
THEN I ALSO SHOWED A TABLE FROM
THE CDC, THESE ARE NOT MY
FINDINGS, THESE ARE PULLED FROM
CDC, AND PUBLISHED WORK FROM
BRRFS, THAT WHEN YOU'RE LOOKING
AT INSOMNIA SYMPTOMS WHICH I
THINK I DID ELUCIDATE THAT IN
NONMETROAREAS WHICH DOESN'T
NECESSARILY MEAN THEY ARE RURAL
AREAS, THAT THERE IS A
LIKELIHOOD THERE WILL BE RURAL
AREAS, THAT THERE WAS A HIGHER
DEGREE OF ISSUES REGARDING
PROBLEMS FALLING ASLEEP,
PROBLEMS STAYING ASLEEP.
SO I UNDERSTAND THE QUESTION
IT'S NOT NECESSARILY A
DISCREPANCY PER SE IN HOW I
PRECEPTED IT, THE DISCREPANCY
ITSELF EXISTS AND SO THIS WHERE
I WAS SAYING IT'S AN OPPORTUNITY
FOR US TO DIVE IN DEEPER TO
UNDERSTAND WHY IS IT THAT SHORT
SLEEP DURATION SEEMS TO BE
RELATIVELY THE SAME IN URBAN AND
RURAL AREAS.
NOW I THINK FROM AN ETHICAL
STANDPOINT, THE POSITIONING OF
URBAN AND RURAL DIVIDE IS
LOOSELYITATED LY--LOOSELY STATEY
NOT CAPTURE THE NUANCES IN
DIFFERENCES WHICH IS WHY I
STARTED OUT MY PRESENTATION
ABOUT HAVING THOSE
OPERATIONALIZED LENS TO BE ABLE
TO IDENTIFY DIFFERENCES VERSUS
DISPARITIES.
SO WITH THE GIVEN TIME,
OBVIOUSLY I'M MORE THAN HAPPY
AND THANKS FOR THE OPPORTUNITY
TO FURTHER ELUCIDATE, BUT THOSE
ARE SOME OF THE KEY NOT
NECESSARILY DIFFERENCES BECAUSE
SLEEP DURATION IS DIFFERENT FROM
INSOMNIA, THAT THERE ARE
OPPORTUNITIES FOR US TO DIVE IN
DEEPER TO FURTHER ELUCIDATE THE
FEATURED THAT I HIGHLIGHTED WERE
NOT FEATURED THAT PARTICULARLY
SAID THAT IS THE REASON WHY
SPECIFICALLY URBAN AND RURAL
AREAS MAY BE MORE LIKELY.
THESE ARE POTENTIAL FACTORS THAT
FOLKS IN THE PAST, NOT JUST IN
THE U.S. BUT SOME OF THESE
STUDIES ARE SUPER OTHER AREAS
LIKE CHINA AND INDIA WHERE THERE
IS A GREATER CONCENTRATION OF
RESEARCH IN RURAL AREA ANDS
THERE'S MORE DATA THERE THAT WE
CAN CERTAINLY BORROW FROM, TO
REALLY ROLL OUT A MORE ROBUST
RURAL SLEEP HEALTH RESEARCH
PROGRAM.
SO THANK YOU SO MUCH FOR THE
OPPORTUNITY TO CLARIFY.
>> FREIGHT, THANK YOU SO MUCH,
AND THAT'S THE POINT THEY REALLY
WANTED YOU TO GET TO DRIVE HOME.
SO THANK YOU SO MUCH FOR
ANSWERING THAT.
ARE THERE ANY OTHER QUESTIONS OR
DO ANY OF THE PANELISTS HAVE
QUESTIONS OF EACH OTHER?
>> I HAVE A QUESTION FOR
DR. KIOMORTZOGLOU, IF I MAY ASK.
THANKS FOR THE PRESENTATION ON
GEOSPATTIAL EFFECTS ON HEALTH
AND WE'RE STARTING TO UNDERSTAND
THE GEOSPATTIAL EFFECTS
DISTRIBUTION OF HEALTH CENTER
FOR EXCELLENCE ON CENTER
FOR EXCELLENCE ON AGING--HEALTH
SLEEP AND DISPARITIES FOR SURE,
AND IN THAT REGARD, THE GENERAL
AREA OF GEOGRAPHY AS YOU THINK
OF AS A NEIGHBORHOOD BECAUSE
PARTICULARLY NEIGHBORHOODS IN
URBAN ENVIRONMENTS THAT COULD
BEING DRAMATIC DIFFERENCES, SO
THEY'RE CLOSE TO EACH OTHER IN
TERMS OF MILEAGE AND THE CITY,
SO I GUESS IT'S A LONG WINDED
QUESTION BUT THE QUESTION IS
WHAT KIND OF RESOLUTION CAN WE
GET IN THOSE SITUATIONS, BECAUSE
SOME OF THESE DIFFERENCES AMONG
THESE DIFFERENT NEIGHBORHOODS IN
URBAN AREAS MUST BE DUE TO SOME
KIND OF--OR AT LEAST PARTIALLY
DUE TO SOME ENVIRONMENTAL
FACTORS SO CAN WE GET THAT KIND
OF RESOLUTION TO LOOK AT THOSE
KINDS OF QUESTIONS AND URBAN
ENVIRONMENTS.
IS THAT WHAT WE CALL CENSUS
TRACT OR FINDING A LEVEL OF
RESOLUTION?
THANKS.
>> ABSOLUTELY.
THANK YOU.
EXCELLENT QUESTION.
IT VARIES FROM DATA SET TO DATA
SET AND IT ALSO VARIES FROM
POLLUTANT TO POLLUTANT.
IF WE'RE THINKING ABOUT
SOMETHING LIKE TEMPERATURE,
WHETHER WE HAVE AT THE ADVERSE
LEVEL TEMPERATURE CAN BE--RIGHT
NOW THERE ARE MODELS IN THE
NORTHEAST THAT PREDICT HOURLY
TEMPERATURE AT I THINK SOMETHING
50-METERS, TWEBT METERS HIGHLY
RESULT, RIGHT SO WE CAN HAVE AT
THE RESIDENTIAL LEVEL OR WE CAN
HAVE SOMETHING [INDISCERNIBLE]
OR EACH A ZIP CODE BECAUSE OF
THE HOMOGENERATED --HOMOGENERATD
AITY MIGHT BE LARGER BUT CERTAIN
POLLUTANTS THAT MIGHT BE
SPATIALLY RELEVANT AS WELL, ALSO
HOMOGENIUS, THEN ZIP COULD YOU
TELL MIGHT BE OKAY, BUT THERE
ARE OTHER EXTENTS--LIKE NOISE
POLLUTION, AIR POLLUTION, LIGHT
POLLUTION, THOSE TEBD TO BE VERY
HETEROGENIUS IN SPACE AND CAN
VARY A LOT BETWEEN 2 PARALLEL
STREETS SO WE CAN GET A WAY FROM
WITH THE CENSUS TRACT AVERAGE,
IT'S NOT IDEAL, IDEALLY WE WOULD
WANT SOMETHING FURTHER.
FINALLY, THERE ARE DATA
DEPENDING ON WHERE, THE MAIN
NITROGEN DIOXIDE MODELS IN THE
UNITED STATES, AND NITROGEN
DIOXIDE IS TRAFFIC TRACER FOR
EMISSIONS, TRAFFIC
CERTAINLY--CERTAINLY MISSIONS,
SO I THINK THE NATIONWIDE MODEL,
THE BEST PERFORMING 1 IS
AROUND 1 KILOMETER RESOLUTION,
IT'S A BIT LARGE BUT WHAT'S ALSO
IMPORTANT IS THE STUDY AREA OF
INTEREST, IS MY COHORT WITHIN A
NEIGHBORHOOD ONLY THEN I
DEFINITELY NEED MORE RESOLVED
DATA OR AM I DOING THE
NATIONWIDE, OR A STATEWIDE
ANALYSIS, IN WHICH CASE, YOU
KNOW, EVEN IF THERE'S SOME
SPECIFICATION BECAUSE I DON'T
HAVE ADDRESS LEVEL DATA OR DO
FINALLY RESOLVE AND HAVE ZIP
CODE LEVEL DATA, THE CONTRAST
WILL KEEP THEM RUNNING SO THAT'S
FINE.
THERE ARE LOTS OF THINGS TO
CONSIDER, I HAD A THOUGHT AND I
MISS TODAY, OH THERE ARE ALSO IF
WE ARE WITHIN--I KNOW FOR
EXAMPLE, NEW YORK CITY HAS
MAIRKSZING DATA, IF THE ANALYSIS
IN NEW YORK STAR AIR POLLUTION,
WE HAVE DATA 300 SQUARE METERS,
CAVEAT THEIR ANNUAL PREDICTIONS
AND NOT DAILY SO THERE'S ALWAYS
THIS BALANCE THAT DAILY
PREDICTIONS EXTENDS NOT ALWAYS
BUT SOMETIMES ARE MORE COARSELY
RESOLVED THAN ANNUAL AND THE
OTHER QUESTION IS, AGAIN, THAT'S
THE IMPORTANCE OF SPEAKING TO
EXPERTS IS CAN WE BRING IN EX
PERS TO IMPROVE THE ASSESSMENTS
IN THE SPECIFIC LOCATION THAT I
AM INTERESTED IN, CAN I
COMPLEMENT THE DATA SOMEHOW,
JUST FOR MY COHORT JUST GIVEN
THE AVAILABLE LOCAL DATA.
I HOPE THIS ANSWERS THE
QUESTION.
I KNOW IT DID NOT ANSWER THE
QUESTION BUT I HOPE IT HELPED
THINK ABOUT THE QUESTION.
HOPEFULLY.
NTHANK YOU.
YES, GO AHEAD AND WITH A QUICK
QUESTION N1 MORE THING, FOLLOW
ON, SORRY, SO YOU RAISE THE
ISSUE THAT THESE DIFFERENT
ELMETROPOLITANS OF EXPOSURE HAD
DIFFERENT SPATIAL RESOLUTION SO
IF YOU'RE REALLY INTERESTED IN
THE COMBINED EXPOSURE OF ALL
THESE THINGS IN 1 GEOGRAPHICAL
AREA, IT MIGHT GET COMP LIT
KAYED IN TRYING TO COM--COMP
C
OMPLICATED TO COMBINE THE
DATA NTHE SOLUTION IS NOT--NIF
THEY OVERLAP NYES, EXACTLY.
BECAUSE NO MATTER WHAT I HAVE
THE RESOLUTION IS, IF I HAVE
ADDRESS, 1 DATA CAN BE AT ZIP
CODE LEVEL, ANOTHER CAN BE AT
THE COUNTY LEVEL, ANOTHER DATA
SET CAN BE AT THE STREET LEVEL,
I CAN ASSIGN WHATEVERERALUE MY
STREET LEVEL HAS WHETHER IT'S
THE ZIP CODE AVERAGE OR COUNTY
AVERAGE, RIGHT?
SO THAT'S NOT SO MUCH A CONCERN,
AND THEN EVEN IF THEY ARE
DIFFERENT--1'S ZIP CODE, 1'S
CENSUS TRACT, WE CAN HARMONIZE
AS WELL.
>> GREAT.
>> NO, NO, THIS IS GREAT.
AND THANK YOU TO MY ESTEEMED
PANELISTS.
WHAT SHE MENTION SIDE SOMETHING
I WAS GOING TO UNDERSCORE,
SOMETHING WE'RE BUILDING AS
WELL, I'LL USE THIS OPPORTUNITY,
YOU KNOW TO ENCOURAGE THE NIH,
WHICH IS WHY WE'VE CREATED THIS
PRECISION ON PERSONALIZED
POPULATION ON HEALTH, IS THAT AS
YOU ARE GATHERING DATA AT THE
MACROLEVEL, EVEN IF YOU DRILL
DOWN TO THE ZIP CODE OR CENSUS
TRACT, IT'S STILL MACROWHICH IS
WHY WE BELIEVE IT'S IMPORTANT TO
GATHER DATA AT THE INDIVIDUAL AT
A HOME LEVEL AND TO TRY
INCASSULATE AND THAT'S WHAT
WE'RE WORKING ON AS WELL BECAUSE
THE ASSUMPTION AS MY ESTEEMED
PANELISTS MENTIONED THAT
WHATEVER YOUR QUALITY EXPOSURE
OR WHATEVER, YOU KNOW KIND OF
EXPOETIC SOMMIC TOXIC EXPOSURE
THERE THERE'S AN ASSUMPTION THAT
THERE'S AN AVERAGE AND THE
YEAR'S AVERAGE, NOT NECESSARILY
IN REALTIME AND I THINK THAT'S
WHERE WE REALLY NEED TO DO SOME
MORE INVESTMENTS, PARTICULARLY
WHERE WE'RE SEEING, WHAT WE'RE
TALKING ABOUT IS, WE NEED MORE
FORMS OF NOT JUST CLOUD COMPUTE
BUT ALSO EDGE COMPUTE TO BE ABLE
TO CAPTURE THOSE DATA IN
REALTIME, NOT ONLY FROM A
RESEARCH STANDPOINT BUT FROM A
CLINICAL STABBED POINT WHERE IF
A CHILD HAS ASTHMA OR SOME
INDIVIDUAL HAS A RESPIRATORY
ISSUE, THAT CAN BE CRITICAL AND
I THINK THIS IS WHERE OUR
RESEARCH, THAT WE'RE PROPOSING
CAN REALLY PROVIDE THAT ROADMAP
AS TO HOW TO BUILD OUT THE
INFORMATICS INFRASTRUCTURE AS
WELL AS DATA INFRASTRUCTURE TO
COLLECT THOSE DATA IN REALTIME
AS WELL AS PROCESS THEM AND TO
MAKE MEANING OF THEM.
>> THANK YOU.
DOES ANYONE ELSE HAVE A
QUESTION.
IF NOT I HAVE 1 QUICK 1 FOR ANY
OF YOU ON THE PANEL ESPECIALLY I
THINK DR. BRIDGES.
WITH ALL OF THE HYPE AND NEWS
AROUND AI AND THE POTENTIAL FOR
HARM AND POTENTIALLY MARGINAL
POPULATIONS THAT THE ALGORITHMS
THAT THESE THINGS ARE TRAINED
ON, YOU KNOW WE'RE LOOKING AT
THESE VERY DIVERSE POPULATIONS,
WHETHER IT'S IN THE U.S. RURAL
VERSUS URBAN AND VERY DISTINCT
DIFFERENT GROUPS OF PEOPLE BUT
ALSO IF YOU'RE THINKING ABOUT
RURAL VERSUS, VURRAL AND U.S.
VERSUS CHINA.
HOW ARE YOU GOING TO MAKE--HOW
CAN WE COME TO TERMS WITH THAT
AND KNOW THAT THE TRUTH THAT WE
REALLY ARE GETTING IS REALLY THE
TRUTH AND NOT SOMETHING THAT'S
BIASED BY THE ALGORITHMS OR THE
THINGS WE'RE TRAINING AND
EXKIEWZ MY CRUDE LANGUAGE, THIS
IS NOT MY EXACT FIELD BUT I'M
JUST DANGEROUS ENOUGH TO KNOW A
LITTLE.
>> I THINK THE ISSUE IS A BIT
MORE FUNDAMENTAL THAN THAT.
SO THE ISSUE OF INCREASE OF BIAS
IN DATA SETS IS STILL VERY MUCH
AN ACADEMIC 1.
WE DON'T EVEN KNOW IN MANY OF
THE INSTANCES THAT THE COMMUNITY
WANT OR WILL USE THE TECHNOLOGY
EVEN BEFORE YOU ASSESS THE
POTENTIAL BIASES INVOLVED IN THE
DATA, YOU HAVE TO ASK WHAT IS
THE GENERAL LEVEL OF COMMUNITY
SUPPORT FOR THE TECHNOLOGIES,
SURPRISINGLY, THERE ARE OTHER
AREAS WHERE PEOPLE CAN BE VERY
FORGIVING OR ACCEPTING THAT IT
MIGHT BE, YOU KNOW NOT INCLUDING
THEM OR IT COULD BE THE BEST USE
OF THE DATA THAT'S AVAILABLE,
AND SOMETIMES PEOPLE ARE VERY
HAPPY TO HAVE THEIR DATA USED.
WE SOMETIMES GET VERY FOCUSED ON
DATA PROTECTION, BUT IF YOU
APPROPRIATELY PARTNER, I THINK
THAT'S IN OF THE ISSUES
ASSOCIATED WITH YOU KNOW WHAT
WE'RE HEARING IN PC ORI, OR AT
AHRQ, HAD IS THIS DEVELOPING
LONG-TERM MEANINGFUL
PARTNERSHIP, SO WHEN THE
COMMUNITY IS A PARTNER IN THE
DEVELOPMENT AND CAN PROVIDE
OVERSIGHT, ON THE DEVELOPMENT
AND POTENTIAL IMPLEMENTATION,
THEN I THINK--THAT'S OUR FIRST
ORDER EFFECT AND I THINK THE
ISSUES OF IMPLICIT DATA BIAS IS
A SECOND ORDER ISSUER EFFECT SO
I WOULD SAY, IS A MUCH GREATER
URGENT NEED UNLESS WE JUST LIKE
IN MEDICAL PRODUCT DEVELOPMENT
CAN ACCEPT THAT MAYBE ONLY 1 IN
1000 TECHNOLOGIES COME TO MARKET
AND ARE SUCCESSFUL, BUT,
CONGRESS AND OTHER GROUPS HAVE
BEEN VERY ADAM ANT THAT THAT'S
NOT GOOD ENOUGH AND WE'RE SEEING
MAJOR CHANGES IN BIOLOGICS FOR
RARE DECS, ADVOCACY AND
ENGAGEMENT OPERATOR DISEASE
GROUPS THROUGH PEAK
ORGANIZATIONS, PATRON
ORGANIZATIONS WHERE THEY NOW
BECOME PROFESSIONAL ENTITIES, I
THINK THAT'S REALLY GOOD, I WE
HAVE TO START ENGAGING AND
HAVING PATIENT VOICE IN WHAT
WE'RE DOING IS I INCREASINGLY
ASK AI AND BIG DATA RESEARCHERS
WHERE'S THE PATIENT VOICE IN
HERE AND THEY WENT, WELL, I
COULD INCORPORATE IT.
THEY MAKE ASSUMPTIONS ABOUT WHAT
THE PEOPLE WANT WITHOUT DOING
THE LEG WORK TO ACTUALLY
UNDERSTAND THAT.
IN ANOTHER PROJECT I'M DOING WE
ARE COLLECTING BIG DATA IN
PATIENTS WISHES AND COMMUNITY
ENGAGEMENT BUT UNFORTUNATELY,
THEY HAVE TO GO GENOMIC DATA,
HAS TO GO IN 1 DATABASE AND THE
PUBLIC WISHES HAVE TO GO IN
ANOTHER DATABASE SO WE HAVE TO
DECOUPLE GOING TO GREAT LENGTHS
TO HAVE THE COMMUNITY VOICE
PAIRED WITH THE DATA THAT WE'RE
CLICKING BUT CURRENT DATA IS
PULLING THOSE 2 THINGS APART, SO
I THINK WE HAVE TO TAKE A HARD
LOOK AT OURSELVES, NOT JUST ON
THE ACADEMIC ISSUES, BUT ON THE
REAL NEEDS AND DESIRES OF THE
COMMUNITY AND BRING THEM ALONG
AS PARTNERS.
>> THANK YOU SO MUCH.
I KNOW WE'RE COMING CLOSE TO THE
END, SO, FOR AZIZI, AND MARRIANA
IF YOU HAVE A COMMENT TO THIS,
IF YOU CAN KEEP IT TO LESS THAN
A MINUTE, A MINUTE OR LESS EACH,
GO AHEAD AND ADD YOUR COMMENTS.
>> I WILL HAVE HER GO FIRST,
PLEASE NTHANK YOU AZIZI.
NLET ME LOWER MY HAND.
YES, ABSOLUTELY AGREE,
COMMUNITIES THIS SHOULD BE
INVOLVED TIMES WE DEVELOP THESE
TOOLS USING AI INDEPENDENTLY OF
COMMUNITY, THE MODELS I
DEVELOPED ARE NATIONWIDE AND WE
KNOW THAT THE SOURCES ARE NOT
RANDOMIZED TRIAL AMLY
DESCRIEWBED, WE KNOW THAT
CURRENT MONITORING SITES ARE
RANDOMLY DRIEWBED SO I THINK
IT'S ON THE RESEARCHERS SIDE TO
MAKE SURE THAT WE TEST WHAT WE
DEVELOP.
WE DO OUR BEST, WE TALK WITH
EXPERTS, WE FIND VALIDATION DATA
SETS THAT ARE INDEPENDENT OF THE
DATA SETS THAT WE ARE USING TO
DEVELOP THE MODELS AND WE TEST
UNSUPPORTS AS I SHOWED.
WE KNOW THAT CERTAINTY
COMMUNITIES HAVE MUCH HIGHER
UNCERTAINTIES THAT THAT'S
EVIDENCE OF THE LACK OF THE
FAIRNESS IN PREVIOUS MODELS IF
YOU WOULD LIKE.
SO IT'S ALL ON US, I THINK WE
HAVE A VERY BIG RESPONSIBILITY
HERE, ON US AND OUR PEERS, TO
MAKING--YOU KNOW, WE KEEP EACH
OTHER ON TRACK.
SORRY.
>> YEAH, SURE, THANK YOU.
GREAT, GREAT, SEGUE AND JUST TO
HIGHLIGHT WHEN WE'RE TALKING
ABOUT BIAS, BIAS IS NOT A
MONOLITHIC TERM, THEY'RE 9
DIFFERENT FOR THE PURPOSES OF
BIASES IN AI FROM SELECTION TO
OVERLIERS, OBSERVER BIAS
EXCLUSION, BIAS RACIAL, ABOUT I
AS IN ASSOCIATION, BIAS AND THE
REASON WHY I HIGHLIGHT THOSE IS
THAT IF WE ARE GOING TO GET VERY
SERIOUS ABOUT TACKLING BIAS IN
AI, THAT WE NEED TO DECOUPLE
MANY OF THE DIFFERENT BIASES
THAT ARE INHERENT, WHAT I WOULD
HIGHLY RECOMMEND AND ENCOURAGE
US TO DO IS THAT BIAS IS NOT AT
THE LEVEL OF THE ALGORITHM BUT
IT'S ALSO AT THE LEVEL OF THE
SENSOR AND THE DEVICE WHICH IS
WHY ANY SOLUTION THAT WE UTILIZE
IN OUR STUDY THAT WE HAVE--IT
HAS TO PASS THE POPULATION
HEALTH VALIDITY TEST THAT WE'VE
DEVELOPED, WHICH PARTICULARLY IN
A PULSE OX SYMMETRY IT MUST BE
UTILIZED IN PEOPLE WITH DARKER
SKIN PIGMENTATION, THAT IS
CRITICAL BECAUSE THIS WILL HELP
WITH THE ENTIRE WORK FLOW IN
TERMS OF THE QUALITY OF THE DATA
AND HOW IT'S PROCESSED AND THE
LAST POINT IS ABOUT THE
ONTOLOGY, THE KNOWLEDGE GRASP
THAT UNDER GIRD MANY OF THESE AI
ALGORITHMS THAT ARE CRITICAL, I
KNOW 1 OF MY MENTEES, HUGE SHOUT
OUT TO HER, SHE WORKING ON THOSE
LOOKING AT SLEEP HEALTH
DISPARITIES IN NLP NATURAL
LANGUAGE PROCESSING AND WE'RE
GOING AT THE ROOT LEVEL TRYING
TO FIND BETTER WAYS IN WHICH WE
CAN UNROOT BIASES IN THESE
ONTOLOGIES SO THAT WE CAN HAVE
REDICTIVE MODELS AND NATURAL
LANGUAGE ALGORITHMS BE MORE
REFLECTIVE AND MORE ACCURATE AND
MORE REPRESENTATIVE.
THANK YOU.
>> THANK YOU.
THOSE ARE GREAT POINTS.
THIS IS WAY TO SHORT OF A TIME
FOR THIS CONVERSATION AND I HOPE
WE CAN KEEP THIS AND I'M SURE
KEEP THIS CONVERSATION GOING ON
OFFLINE.
WITH THAT, I AM GOING TO LET US
MOVE ON TO THE SUMMARY AND
CONCLUSIONS WHICH I BELIEVE IS
BY THE CHAIRS OF THIS MEETING,
SO I WILL TAKE MYSELF OFF CAMERA
AND IF THE CHAIR CAN COME ON, I
AM HANDING THIS OVER TO YOU ALL.
>> YEAH, THANK YOU DANA, THANK
YOU FOR THE GREAT DISCUSSION, I
WISH WE COULD CONTINUE IT
FURTHER BUT PERHAPS IN THE
FUTURE.
>> YES, SO, I THINK I'M GOING TO
KICKOFF THE FINAL REMARKS AND I
RECOGNIZE WE ARE ABOUT 20
MINUTES BAOF THE THE CONCLUDING
TIME SO I WILL MAKE IT SUPER
QUICK BUT I WANT TO THANK
LAWRENCE FOR CONCEIVING OF THIS
INCREDIBLE WORKSHOP, IT'S BEEN A
TRUE WHIRL WIND FROM LIKE
STARTING WITH PUBLICLY AVAILABLE
DATA RESOURCES TO LOOKING AT
MEASUREMENT ISSUES, METHODS, THE
DEMOGRAPHIC AND DEGEOGRAPHIC,
THERE HAVE BEEN SO MANY
INCREDIBLE TOPICS INCLUDING, YOU
KNOW SLEEP DISORDERS AND HEALTH
OUTCOMES AND LIKE SUCH A RANGE
AND I WAS INSPIRED BY AZIZI 'S
START WHEN HE SAYS I'M SO
GRATEFUL BECAUSE I AM ALSO SO
GRATEFUL THAT WE HAVE THIS
COMMUNITY AND HOPE THAT WE CAN
CONTINUE TO WORK TOGETHER AND
MAKE CONNECTION ANDS HOPEFULLY
MEET UP IN HOUSTON OR OTHER
SLEEP MEETINGS AND I WAS
REMINDED DURING THESE PAST FEW
DAYS, OF ABOUT 20 YEARS AGO, I
SAW A TALK ABOUT DR. STICKGOLD,
WHO USED TO RUN THE CENTER FOR
SLEEP AND COGNITION AT HARVARD,
AND HE ENDED THIS TALK WITH SOME
PICTURES OF AFRICAN AMERICAN
KIDS SLEEPING IN ROOMS THAT WERE
CROWDED OR CLUTTERED AND HE SAID
YOU KNOW WHAT LEAP IS SO
IMPORTANT FOR LEARNING BUT WE
DON'T KNOW ANYTHING ABOUT THE
DEMOGRAPHICS OF SLEEP.
AND HE SAID THAT TO ME AND THAT
INSPIRES ME TO SAY, HEY, WHAT
WOPPULATION BASED DATA IS
AVAILABLE TO LOOK AT DISPARITIES
IN SLEEP AND THE DEMOGRAPHICS
OR, YOU KNOW, DEMOGRAPHY OF
SLEEP AND AT THE TIME THERE WAS
VERY LITTLE, THAT'S WHEN I
LOOKED AT NHS, AND NHANES BUT IF
YOU ATTEND TODAY OR PAST 2 DAYS,
THERE'S SO MUCH MORE, SO MUCH
MORE OPPORTUNITY AND MY CALL FOR
ALL OF YOU IS TO LEARN FROM EACH
OTHER AS I'VE LEARNED FROM YOU
AND SAY HOW CAN WE HARNESS ALL
OF THIS EXPLOSIVE DATA
OPPORTUNITY AND NEW METHODS OF
MEASURING AND ANALYZING DATA FOR
GOOD, TO HELP IMPROVE NOT JUST
SLEEP, BUT BEING ESPECIALLY AT
THE MOST VULNERABLE POPULATION
EMPLOY SO I THINK WE ALL CARRIED
THAT SORT OF UNDERLYING MESSAGE
WITH US OVER LAST 2 DAYS BUT
WHAT I WANT US TO DO IS MAKE
SURE THAT WE CARRY IT FORWARD
AND STAY CONNECTED AND I THINK
SEAN HAD A COUPLE OTHER REMARKS
AND THEN WE'RE GOING TO HAVE
DR. BAZER WRAP US UP WITH WHAT
THE GOALS ARE FOR NHLBI AND WHAT
THE NEXT STEPS ARE.
SO SEAN, DO YOU WANT TO POP ON?
>> SURE.
SO YEAH, THANK YOU.
SO I AGREE WITH ALL OF THOSE
POINTS AND I GUESS I'LL QUICKLY
JUST TRY TO TAKE THE CO-CHAIR'S
PREROGATIVE TO HIGHLIGHT 1 THING
THAT TRUCK ME RELATE TOTD FUTURE
DIRECTIONS AND CHALLENGES, SO AS
I SEE IT WE COVERED ADVANCES IN
THE DATA SETS, DEVICES AND
PREDICTIVE NL MODELS AND TO ME,
TO A FIRST APPROXIMATION, MY
SENSE WAS THAT AT LEAST IN THE
RESEARCH SLEEP FIELD, IT'S VERY
SUCCESSFULLY ON TRACK FOR
LEVERAGES MANY OF THESE ADVANCES
AND I THINK IN MANY WAYS IT'S
PROBABLY A NO BRAINER BUT IF A
RESEARCH FIELD CAN AND WILL
BENEFIT FROM AN OLD MINTED
ARSENAL OF MEASURES AND TOOLS, I
THINK GOING FORWARDS 2
PRIORITIES THAT WERE TOUCHED ON,
THE NEED FOR SUSTAINED EFFORTS
AND SUSTAIN ARDIZEATION AS
EVALUATION OF TOOLS AS DATA
SHARING AND JUST AS IMPORTANT AS
WE DISCUSSED, WE NEED ONGOING
WORK TO SIN SURE THAT BOTH
DEVICES AND MODELS PERFORM
EQUIVALENT LYE IN DIFFERENT
POPULATIONS WITHOUT BIAS.
N1 THING I THOUGHT WAS
INTERESTING AND SO IF I HAD TO
MAKE A PREDICTION ABOUT FUTURE
DIRECTIONS AND POTENTIALLY
FORMING ISSUES IN AM CANNING
YEARS BUT I THINK IN ADDITION TO
EVERYTHING THAT DR. BRIDGES
TALKED ABOUT IN THE CLINICAL
CONTEXT, I THINK SOMETHING ELSE
WE MAY FIND OURSELVES TALKING
ABOUT IS REALLY THE ROLE OF
GENERATIVE, RATHER THAN
PREDICTIVE AI IN THIS CONTEXT.
SO THIS OBVIOUSLY WASN'T A FOCUS
OF THIS MEETING AND IT HASN'T
REALLY EMERGED IN SLEEP RESEARCH
YET BUT I WAS--YOU KNOW I WAS
IMPRESSED THAT DR. KATABI'S
EXCELLENT TALK LEFT US WITH A
KIND OF PROVOCATIVE GLIMPSE OF
GENERATIVE AI AND MAKING
PARALENS
PARALLELWITH THE WORLD'S MOST
WELL KNOWN EXAMPLE OF GENERATIVE
AI AND FOR CHAT GPT.
AND WITHOUT A DOUBT THERE'S
FANTASTIC POTENTIAL AND WE'RE
SEEING EMERGE FREE RADICALS
GENERATED OTHER FEEM FIELDS FROM
PROTEIN PREDICTION AND MEDICAL
IMAGES AND SO ON, AND YOU CAN
IMAGINE NONCONTROVERSIAL USES,
GENETIC DATA SETS, DENOISING
IMAGES AND SO ON, AND ACTUALLY
IN SOME SLEEP TAIJING MODELS USE
IT SAY FOR DATA AUGMENTATION TO
HELP TRAIN THE MODELS.
WHY I SAY IT'S THORNY IS IT DOES
IN SOME WAY AND IT BLURS THE
LINE BETWEEN MEASUREMENT OR DATA
AND THEN MODEL OR ANALYSIS, WITH
THIS SECOND ARROW GOING
BACKWARDS BETWEEN THEM AND SO
INHERENTLY FOR US THERE ARE SOME
LIMITS THERE, WE DO APPRECIATE
THE CHAT GPT REFERENCE WAS JUST
AN EXAMPLE, TO FRAME THINGS AND
NOT MEANT TO BE TAKEN LITERALLY
BUT JUST TO USE THAT NONETHELESS
AS THE MEASUREMENT TOOL, RIGHT?
, THE EXACT ANALOGY WOULD
INVOLVE GENERATING 1 PARTICULAR
IMAGE OF A KIND OF PARTICULAR
DOG OR WHATEVER IT WAS, GIVEN
THE LIMITED PROMPT.
AS IT IS OF COURSE IT WOULD
DPENERATE JUST A RANDOM IMAGE
CONSISTENT WITH THE PROMPT BUT 1
THAT WILL BE DIFFERENT EACH TIME
YOU RUN IT SO OBVIOUSLY JUST
DIFFERS FROM THE TYPICAL CONCEPT
OF MORE REFINED MEASURE AM SO
IT'S A PURPOSELY BLUNT STRONG
MAN I BEING GIVE A SLEEP TECH A
BOX OF CRAYONS AND THEY WOULD BE
ABLE TO DRAW ME A SPECTROGRAM
THAT LOOKED SUPERFICIALLY
CONSISTENT WITH A GIVEN HIPIT
GRAM BUT WE WOULDN'T ASSUME THAT
PROVIDES A NEW MEANINGFUL LAYER
OF SOLUTION, I AM NOT SAYING
THIS PARTICULAR MODEL IS DOING
NOTHING MORE THAN THAT, I AM
SURE IT IS AND I DON'T MEAN TO
PLAY DOWN THE POTENTIAL FOR
GENERATIVE AI BUT IT STRUCK ME
AS KNOWING HOW CLOSE WE ARE TO
THAT SUPERFICIAL ANSWER WHERE
SOMETHING IS REALLY ADDING TO TD
DATA, I THINK IS JUST GOING TO
BE INCREDIBLE LOAMACYY
CHALLENGING SO I THINK IT WILL
BE FOR BEYOND PREDICTIVE MODELS,
A WHOLE NEW SET OF CAPABILITIES
BUT WITH THAT INCREASED POWER, I
THINK WILL BE MANY, MANY MORE
CHALLENGES BUT WILL REALLY
PROMPT US TO YOU KNOW THINK
CAREFULLY AND I THINK BE
CAUTIOUS IN ROLLING OUT SOME OF
THOSE TECHNOLOGIES, I WILL END
THERE, AND HAND IT OVER TO LARRY
WHO I WILL JUST THANK ONCE MORE
JOINING MY CO-CHAIR FOR
ORGANIZING THIS MEETING WHICH
HAS BEEN IMRAIT NTHANK YOU SO
MUCH TO THE CO-CHAIRS AND THE
WONDERFUL SPEAKS AND EVERYBODY
THAT PARTICIPATE INDEED IN
MEETING.
I AM ECHO LAUREN'S KIND OF
CONCLUSION, TAKE HOME MESSAGE
FROM THIS TAKE HOME MEETING FOR
ME ANYWAY AND HOPEFULLY FOR THE
NIH AS WELL WITH REGARD TO SLEEP
HEALTH, PARTICULARLY BUT MAYBE
WITH REGARD TO HEALTH OVERALL,
IT BEINGLY FOR THE SESSIONS
TODAY WE HEARD ABOUT THE VAST
NUMBER OF INDIVIDUAL INFLUENCES
AND ENVIRONMENTAL INFLUENCES ON
SLEEP AND SLEEP HEALTH AND WE'RE
JUST BEGINNING TO UNDERSTAND
WHAT THOSE INFLUENCES ARE AND
IDIDN'TIFY THEM, LET ALONE
FIGURE OUT WHAT IT REALLY MEANS
TO HAVE THOSE INFLUENCES ACTING
IN CONCERT ON AN INDIVIDUAL
POPULATION, WHATEVER, THE
VECTORS BECOME ALMOST
OVERWHELMING, IN TERMS OF THE
AMOUNT OF DATA WE WANT TO
INCORPORATE, THERE, BUT
THANKFULLY WE DO HAVE THE TOOLS,
THAT WERE DESCRIBED YESTERDAY
WHERE WE COULDN'T START THE DEAL
WITH THIS ENORMOUS AMOUNT OF
DATA AND MAKE SENSE OF IT NERMAL
ITS OF RATIONAL OUTCOMES OF WHAT
THESE ENORMOUS NUMBERS OF
INFLUENZS BECAUSE THEY'RE FAIRLY
PREVALENT IN SOME ENVIRONMENTS
CAN HAVE ON HEALTH AND SLEEP
HEALTH IN PARTICULAR, LIKEWISE,
BOTH YESTERDAY, AND TODAY, WE
KIND OF QUESTIONED THE
TRADITIONAL METRICS OF SLEEP
HEALTH AND GOOD SLEEP AND THOSE
KINDS OF THINGS WOO WE WONDERED
THROUGH DISCUSSIONS YESTERDAY
AND ALSO KIND OF SPEILING OVER
INTO TODAY WHETHER WE'RE REALLY
GETTING A COMPLETE PICTURE OF
SLEEP HEALTH AND GOOD SLEEP AND
THOSE KINDS OF THINGS IN
INDIVIDUALS OR IN
POPULATIONSUING THE TRADITIONAL
METRICS WE'RE STILL USING AND
FOR EXAMPLE, 1 THING WE MAY NOT
HAVE PAID ATTENTION TO ENOUGH
AND PATIENT REPORTED OUTCOMES
AND SOME OF THE TALKS HAVE
SHOWN, MAYBE THAT'S AN IMPORTANT
THING TO CONSIDER WHEN WE'RE
KIND OF QUANTIFYING SLEEP
HEALTH, INFLUENCES OF
THERAPEUTICS INFLUENCES OF OTHER
THINGS ON SLEEP HEALTH.
I THINK WHAT WE WANT TO DO WITH
NIH IS GET A COMPREHENSIVE
PICTURE OF THE INFLUENCES ON
SLEEP HEGHT, THESE ENVIRONMENTAL
INFLUENCES AND USE PROBABLY AI
TO DO THAT AND LIKEWISE, GET A
COMPREHENSIVE PICTURE OF WHAT
SLEEP HEALTH IS, AND THERA'S SO
MANY VARIABLES THERE, THAT WE'RE
GOING TO RELY ON THAT MACHINE
LEARNING DATABASE TOOLS, WE
HEARD ABOUT YESTERDAY, TO REALLY
KIND OF GENERATE THIS KIND OF
OVERALL COMPLETE PICTURE OF WHAT
SLEEP HEALTH IS, WHAT DISTURBS
SLEEP HEALTH AND WHAT WE CAN DO
ABOUT THAT IN THE FUTURE AND
HOPEFULLY, THAT'S SOMETHING THAT
COULD BE A FUTURE PRIORITY OF
NIH BECAUSE WE DO HAVE THESE
TOOLS AVAILABLE AND WE ARE
STARTING TO IDENTIFY, WHAT IS IT
WE NEED TO LOOK AT IN THE
ENVIRONMENT AND WHAT WE REALLY
NEED TO ASK ABOUT SLEEP THAT
MAKE ITS GOOD SLEEP, BAD SLEEP,
DISRUPTED SLEEP, SO HOPEFULLY
STAY TUNED AND HOPEFULLY WE WILL
BE LOOKING FOR STIMMULING
RESEARCH IN THAT DIRECTION IN
THE FUTURE EMPLOY AND THAT'S 1
OF THE THINGS I WAS HOPING WOULD
COME OUT OF THIS WORKSHOP IS A
MORE COMPREHENSIVE PICTURE OF
INFLUENCES ON SLEEP AND WHAT
SLEEP REALLY IS AND HOW AI CAN
HELP US UNDERSTAND THOSE KINDS
OF THINGS SEPARATELY IN AN
AGGREGATE AND PROBABLY MOST OF
THE PEOPLE WHO GAVE
PRESENTATIONS YESTERDAY, MAY NOT
BE ON THIS CALL TODAY, BUT WHAT
MY SORT OF SHORT-TERM GOAL FOR
THIS--FOR THE RESULTS FOR THIS
WORKSHOP THIS MORNING, THIS
WONDERFUL WORKSHOP SO MANY GOOD
DISCUSSIONS AND PRESENTATIONS, 1
OF THE SHORT-TERM GOAL IS TO
PUBLISH A MANUSCRIPT SUMMARIZING
RESULTS BECAUSE I REALLY WANT TO
GET THEM OUT THERE SO WE HAVE
RECORDINGS FROM THE SESSIONS, I
HAVE A VERY DILIGENT SCIENCE
WRITER WHO'S TAKING EVERYTHING
DOWN, I HAVE YOUR SLIDES AS A
RESULT, THE RECORDINGS, AND YOU
SENT US ALL YOUR SLIDES AND WE
HAVE THEM ALL IN OUR FILES AT
NIH BUT I TILL MAY HAVE
QUESTIONS, KIND OF NUANCES IN
THE PRESENTATIONS, NUANCES MAY
BE IN MY UNDERSTANDINGS OF YOUR
PRESENTATIONS AS WELL, AND I
WANT TO MAKE SURE I REPRESENT
EVERYTHING COMPLETELY ACCURATELY
AS POSSIBLE, AND GET THIS BIG
PICTURE OF WHAT WE REALLY KIND
OF DISCUSSED AND LEARNED FROM
THIS WORKSHOP AND GET IT
PUBLISHED SO WE CAN DISSEMINATE
AS BROADLY AS POSSIBLE TO PEOPLE
THAT WERE ABLE TO ENJOY THE
WORKSHOP BUT ALSO PEOPLE WHO ARE
NOT ABLE TO INVITE OR BE
INTERESTED IN THESE QUESTIONS,
SO THANKS AGAIN AND THAT'S WHERE
THINGS WILL BE GOING, BOTH IN
THE NEAR FUTURE WITH THE
MANUSCRIPT AND MAYBE, MAYBE
DEPENDING AS WE SAY, ON
AVAILABILITY RESOURCES, AS WE
SEE AT THE NIH, HOPEFULLY IN
TERMS OF STIMULATING RESEARCH IN
THE FUTURE.
SO THANKS EVERYBODY FOR A
WONDERFUL SESSION.
>> ANYTHING ELSE?
SEAN?
LAUREN?
THAIRNGS TO THE WORD, THESE 2
DPIES WHO HELPED SHAPE THE
AGENDA FOR THIS AND KEPT IT
MOVING ALONG, THANKS TO MY
PROGRAM OFFICER COLLEAGUES, NIH,
AT THE VARIOUS INSTITUTES, THAT
ARE INVOLVED IN SLEEP RESEARCH
AND NIH FOR SERVING AS
MODERATORS AND THANKS, THANKS
ABOVE ALL TO THE WONDERFUL
SPEAKERS WE HAD, REALLY OPEN
THEIR EYES TO THE NEW
POSSIBILITIES IN THE FIELD OF
SLEEP, AND SLEEP HEALTH AND BIG
DATA AND THE IRPT SECTION AMONG
ALL THESE DIFFERENT KIND OF
VARIOUS DIFFERENT STREAMS AND
THE INSIGHTS WE CAN GAIN FROM
THAT INTERSECTION.
SO I HOPE EVERYBODY HAS A
WONDERFUL WEEKEND.
IF THERE'S NO MORE COMMENTS, I 6496 04:16:05,717 WILL CLOSE THE MEETING.