October 30, 2024 • 21 mins
PhD student Sunwoo Emma Cho and Dr. Rahul Ghosal unpack their recent article, “Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006” published in the November 2024 (Vol. 99) issue of Annals of Epidemiology. In this study, the researchers quantify the association between daily (temporally varying) distributional patterns of physical activity and cardiovascular mortality risk.
Read the full article here:
https://www.sciencedirect.com/science/article/abs/pii/S1047279724002436
Episode Credits:
- Executive Producer: Sabrina Debas
- Technical Producer: Paula Burrows
- Annals of Epidemiology is published by Elsevier.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Patrick Sullivan (00:12):
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan,Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today we're talking about thearticle "Exploring the
(00:36):
Association Between DailyDistributional Patterns of
Physical Activity andCardiovascular Mortality Risk
Among Older Ad adults in NHANES2003 to 2006.
" And we're talking with two ofthe authors, Dr.
Rahul Ghosal and Ms.
Emma Cho, about this article.
You can find the full articleonline in the November 2024
(00:59):
issue of the journal at www.
annalsofepidemiology.
org.
I'm going to give you a briefintroduction to our guests.
Dr.
Rahul Ghosal is an assistantprofessor of biostatistics in
the Department of Epidemiologyand Biostatistics University of
South Carolina.
His research focuses ondeveloping novel statistical
(01:20):
methods such as functional dataanalysis with applications in
biosciences and alsodistributional data analysis for
modeling, wearable data withapplications in gait aging and
Alzheimer's disease.
Sunwoo Emma Cho is currently aPhD student at the Darla Moore
School of Business University ofSouth Carolina.
(01:40):
Previously, she was enrolled inthe biostatistics PhD program
at the same university.
She holds a bachelor's degreein econometrics and operations
research from Erasmus University, Rotterdam and a master's
degree in information systemsfrom Seoul National University.
Rahul and Emma welcome.
Rahul Ghosal (02:01):
Hi Patrick, good morning.
It's a pleasure to be here andwe're looking forward to this
podcast and discussing thepapers and the details behind it
.
Emma Cho (02:11):
Yeah, thank you for inviting us, yeah.
Patrick Sullivan (02:14):
So, Rahul, we'll start with you.
What was the purpose of thisstudy?
What research questions wereyou really trying to get at?
Rahul Ghosal (02:20):
So we know that cardiovascular disease is one of
the leading causes of death,both worldwide and also in the
US.
So previously a lack ofphysical activity has been shown
to be a prominent risk factorfor CVD mortality.
So we all now monitor our stepsin smartphones and get those
steps in.
But now these traditionalmeasurements of physical
(02:43):
activity previously wasself-reported and based on
various summary level metrics,which had its own drawbacks.
But recent advances in wearabledevice studies provide
continuously monitored andobjectively measured physical
activity data and, making use ofmore advanced statistical
methods, we can have a betterunderstanding of the
(03:04):
implications of physicalactivity in the context of CVD
mortality.
We can do it by consideringboth the timing of the physical
activity and its dailycompositions.
So our research questionbasically was whether these
daily distributional patterns orcompositional patterns of
physical activity, areassociated with cardiovascular
mortality risk among the olderadults and independent of aging.
(03:28):
So that's the research questionwe are trying to answer here.
Patrick Sullivan (03:31):
Great, so walk us through your study design,
then, and the methods that youused to conduct the research
that's reported in the article.
Rahul Ghosal (03:38):
Sure.
So for our study, what we usedwas the accelerated data from
the 2003-2006 National Healthand Nutrition Examination Survey
, also known as NHANES, and thisstudy actually provides
minute-by-minute levelaccelerometer data.
And we focused on the olderadults aged more than 50 years
(03:59):
and their physical activity datawas linked with mortality data
coming from National Death Index, and we censored the subjects
in December 2019 for collectingtheir mortality information.
And then we used this recentlydeveloped statistical methods in
functional and distributionaldata analysis, which is very
close to my research area, andwe obtained, based on this
(04:22):
minute-by-minute physicalactivity data, some temporarily
varying this minute by minutephysical activity data, some
temporally varyingdistributional patterns of
physical activity, and we callthese as L moments, and this can
be essentially thought as kindof robust analogs of traditional
moments like mean or standarddeviation.
But now, because we havemultiple days of data, we can
calculate them in a way suchthat they are varying over the
(04:45):
time of the day, so we cancollect various information,
such as the daily average, thevariability, the skewness and
their kurtosis patternthroughout the various times of
the day.
So now they kind of provide alot more information beyond the
daily average of physicalactivity, and these L moments
were then used as our mainexposure variables in a Cox
(05:08):
model to estimate the hazard ofcardiovascular mortality, while
adjusting for other knownconfounders such as gender, age,
smoking status and coronaryheart disease.
So that was basically themethod we used to answer our
research question.
Patrick Sullivan (05:24):
Great, and I'm going to turn to you for the
next question, and I wonder ifyou could tell us about what the
main findings from the studywas, and how did they align with
the hypotheses that you hadgoing into doing the research?
Emma Cho (05:36):
So in most of like association study, when we
explore relationship betweenphysical activity and CVD
mortality, those will usually gowith mean.
What we found from our study isthat it's not just the average
intensity of physical activityduring the day that matters, but
also the variability ofphysical activity, especially in
(05:59):
the afternoon, could also playa very crucial role to varying
the CVD mortality risk Also,interestingly, we also saw that
the association between dailymean, which is L1 moment, and
daily variability L2 moment ofphysical activity with CVD
mortality were much morepronounced on the weekdays and
(06:20):
it was not really significant onweekend.
So it was like, aligned withour research hypothesis, yeah,
which was surprising in a goodway, yeah.
So we think our studydemonstrated both circadian
rhythm of physical activity plusdaily decomposition can be
useful to design time of day andintensity specific physical
(06:41):
activity interventions toprotect against the CVD
mortality risk.
Patrick Sullivan (06:47):
Thank you.
So have others studied this?
It seems like a veryinteresting and a very specific
hypothesis, and a testable one,which is great.
But have others studied thissame kind of question, and how
do your findings compare withprevious literature on this
subject?
Rahul Ghosal (07:02):
Yeah, that's a very good question.
So our findings actually matchwith the previous literature in
terms of finding a protectiveeffect of physical activity
against the risk ofcardiovascular mortality.
But, as Emma mentioned, most ofthese previous studies were
based on various summary-levelmeasures of physical activity,
such as total activity count ormoderate to vigorous physical
(07:23):
activity, which is definitelyuseful.
However, our results now kindof complements those results and
also provide a deeperunderstanding into the
relationship between physicalactivity and cardiovascular
mortality risk.
By zooming into this physicalactivity pattern, because we
have continuous physicalactivity minute by minute level
(07:44):
we can, instead of using asingle summary level metric,
zoom it into much more andobtain these patterns of
physical activity varying overthe time of day, and so, by
considering its timing as wellas composition, we one of the
key takeaways is, even if youraverage physical activity is
(08:06):
same throughout the day, aperson with lower variability of
physical activity would have alower reserve of physical
activity and therefore would bean increased risk for
cardiovascular mortality.
So I guess the takeaway message,like our research would be like
it's not only the averagephysical activity that matters.
It's also good to havedifferent ranges of physical
(08:28):
activity and particularlydifferent times of the day.
Patrick Sullivan (08:30):
Right.
It seems like, just as a matterof you know, thinking about
some of the epi aspects of this,that one, just having the
objective device reduces someinformation bias, right, because
especially when we ask peopleabout physical activity, there
is a social desirabilitycomponent.
But then you have this kind ofhuge quantity of data that
really reflects much more.
(08:51):
And I wonder if you feel like,could you have gotten to the
issue of the variability withoutthat resolution of data?
If you're just asking peoplelike how many minutes do you
exercise a day, would you haveeven had the right data to ask
the question about thevariability?
Rahul Ghosal (09:09):
Right.
That's a very interestingquestion.
I think what helps in capturingthis variability, and
especially at various timepoints of the day, because we
have such multiple days ofcontinuously measured physical
activity.
I think- so therefore ourvariability estimates are much
more less uncertain.
We know in statistics it's notonly the point estimates that
(09:29):
matter, also the uncertainty ofthe estimate.
And because we have such hugelycollected, intensively
longitudinal data, we cancapture patterns like
variability or skewness withmuch less uncertainty.
So I think that's also one ofthe definite benefit of such
objectively measured data.
And another aspect is therecould be subjective bias
(09:50):
introduced.
People are trying to describethe variability in physical
activity, so I think definitelythat is also taken care of in
such objective assessments.
Patrick Sullivan (09:58):
Yeah.
Great.
Can you talk a little bit aboutsome of the strengths and any
important limitations to yourstudy?
Emma Cho (10:05):
Yeah, sure, I think already Dr.
Ghosal mentioned a lot oftrends and key insights from our
study.
Yeah, as he mentioned earlier,studies on the association
between physical activity andCVD mortality mainly relied on
summary scalar metrics such astotal activity count, or usually
we use survey data and we areasked by threshold-based measure
(10:29):
for specific activity likelight intensity, LIPA, moderate
to vigorous physical activity,and VPA.
But in our study we took it astep further by applying a novel
method, a partially functionaldistribution approach, to dive
deeper into the structural PA ina more detailed, granular way,
(10:51):
a temporally varyingdistribution patterns beyond the
mean.
So, yeah, key insight I thinkfrom our research is the
importance of variability inphysical activity, particularly
in the afternoon.
Yeah, we mentioned which can beleveraged to design more
effective PA interventions thataim at reducing CVD mortality,
which is a very significantproblem and public health
(11:13):
concern in the US and worldwide.
Patrick Sullivan (11:16):
Thanks for that information.
I wonder if you could also talka little bit about the
limitations of your study.
Emma Cho (11:21):
Yeah, so, likewise any study, our research also has a
few limitations.
First of all, the associationswe found between daily physical
activity patterns and the CBDmortality risk don't imply the
causal relationship.
Yeah, still, there could be apossible residual confounding
bias.
Second of all, since we use acause-specific Cox model,
(11:45):
participants who passed awayfrom causes other than CBD were
treated as right-sensor, butsome of those individuals might
have underlying cardiovascularconditions that we didn't
account for, which couldintroduce some kind of bias in
our estimates.
And lastly, yeah, there couldbe a further methodological work
(12:05):
that can more directlyincorporate survey-weighted
causal survival model to exploreas a further study.
Patrick Sullivan (12:19):
Great.
So now we're going to move onto a section of the podcast that
we call Behind the Paper, andwe really think it's important
to recognize that our methodsare analytical and our process
is scientific, but this work isultimately done by people, and
especially the relationshipsbetween co-authors and how we
work together.
So I just want to ask each ofyou a couple questions to
(12:40):
understand more about how youwork on this project together
and how that sort of fit intoyour mentoring relationship.
So, Emma, I'll start with youand just ask what's your
favorite experience been in yourPhD program so far?
Emma Cho (12:55):
Uh, so far- actually, my initial goal to join the
biostat program was likedeveloping, apply the
computational method thateffectively bridge medicine or
probably carries overstatistical or data science
methodologies.
Yes, through this program I'vebeen able to explore new
methodologies, especially forthis study.
(13:16):
Yeah, we can see more deep ofthe new data structure, new data
frame with new methodologicalstudies.
So, yeah, it was mostfascinating part.
I was in the PhD program andthen I worked as a hospital
(13:37):
researcher for a while, so I sawall the moments that come
together and then, yeah, Irealized I can develop further
study based on the program Ilearned.
Patrick Sullivan (13:50):
Yeah, those moments when all the pieces come
together are like the days thatwe work for for sure.
Is there anything that you wishyou knew before you started the
PhD program?
Emma Cho (14:00):
Yeah, actually I was interested in a lot of different
sources and data structure.
Also, like because usually ifwe when we like dealing with the
table data, it does not giveall information.
So I was interested in otherobjective measures such as like
variables or so image and textand yeah, those all new
(14:23):
methodology curve yeah, that wasI think it was.
I want to dig into, know moreabout.
Like to improve better medicineresearch.
Patrick Sullivan (14:34):
Great.
Okay, and Rahul, we're going toturn back to you now for
another question, so I'm goingto call you Dr.
Ghosal for this one, becauseI'm asking you like, in your
role working with Emma as heradvisor, what was that
experience like and what advicedo you have for first time
mentors who may be working withstudents about how to enter into
that mentoring relationship?
(14:56):
So actually Emma was kind of myfirst student when she started
working with me and I franklyhad a great time working with
Emma.
She's like an excellent listenerand also has a natural kind of
scientific curiosity as aresearcher, apart from her
excellent skills in coding.
So I really enjoyed working withher, like our meetings, and the
(15:18):
project eventually turned outto be a lot of fun and actually
we are also continuing ourcollaboration and exploring some
interesting new directionsbased on the findings we've seen
in this study.
So definitely I had a very goodexperience working with her and
(15:41):
regarding my advice tofirst-time mentors, it would be
to have a good balance betweenbeing hands-on and also giving
the students their own space fortheir own independent research
and exploration.
And we can all probably kind ofuse our personal experience
because it was not so long agothat we were students ourselves,
so we can identify that, whataspects we needed most from our
(16:02):
mentors, and we can apply thosein our day-to-day dealings or
the meetings with the students.
Also, it helps to understandthe motivation, the goals of the
student and connect with themnon-academically too, and when
you are a mentor and I have seenthat worked a great bit in my
experience working with thestudents.
Great.
So thanks for sharing that fromboth your perspectives about
(16:24):
just how it was to work together.
I'm also really alwaysinterested in how the work is
done on these papers, so Iwondered if you could say a
little bit about what roles eachof you played in the like the
conception, the analysis, thewriting, the submitting.
How did you divvy up that work?
Rahul Ghosal (16:43):
So actually we had a great team behind this one.
Emma was obviously the drivingforce behind the study and she
contributed in the research interms of formal investigation,
doing all the analysis,validation, visualization and
software and also writing.
But also behind this we hadsome amazing colleagues of mine
(17:04):
working.
I should mention the name of Dr.
Marcos Matabuena.
So he is currently a postdoc inHarvard and expert in this
functional data analysis areaand he has a lot of experience
working with this enhanced data.
So he was kind of our go-to guy, particularly in terms of the
data aspects of it.
Dr.
Jingkai Wei, who is anassistant professor currently at
(17:26):
the University of Texas HealthScience Center at Houston.
So he's an expert incardiovascular disease
epidemiology, cognitive agingand dementia.
So he was kind of the domainexpert and the epidemiology
expert in this study and he kindof definitely helped us in the
conceptualization and thewriting aspect of the study.
And finally, Dr.
(17:46):
Enakshi Saha is also currentlyassistant professor at the
University of South Carolina.
She is an expert in Bayesianstatistical methods and
functional data analysis andmachine learning.
So she also helped in theconceptualization, the writing
part and the supervision of theproject.
And whenever I needed myco-authors they were there for
me and, like, helped as the bestto their capacity.
(18:09):
So I think we had, as Imentioned, a great team on this
one, and I think that is howgood science should be done
working with a great team.
So definitely I'm grateful toall of them.
Patrick Sullivan (18:23):
It's definitely the best science and
I think it's more fun and justsort of professionally
fulfilling, like in two ways.
One, to interact withcolleagues, but also that I
think the best research rarelysits in one person's domain of
expertise, and so when you talkabout what each person brings
and sort of crossing over,sometimes you get into an
analysis and then you realizelike, oh, there's a type of data
here that I am not quite surehow to handle, and so then
(18:44):
finding that colleague whosewhole thing is handling that
kind of data.
I do think that, as much as wemake an analysis plan, those
things have to evolve, and sosometimes the authorship list
may also either grow or you know.
Rahul Ghosal (18:58):
Definitely, definitely.
Yeah, I agree.
And sometimes, yeah, itdefinitely helps to have like
experts at their respectivefields working with you.
And definitely yeah, whileworking on this project, I also
began to appreciate like being apart of a team and working on
to solve a common researchquestion.
I think that's really powerful.
So I really enjoy when workingthis type of projects now where
(19:21):
I can bring some of mymethodological work to solve
like interesting scientificanswer, scientific questions,
and definitely looking forwardto doing much more of it in
future.
Patrick Sullivan (19:30):
Great.
So Dr.
Ghosal, Ms.
Cho, do either of you have haveanything else you'd like to
share with our listeners?
Emma Cho (19:38):
Yeah, I think I forgot like one of important, my
enjoyable moment from my PhD.
Actually, it was Dr.
Ghosal, yeah, yeah, becauseyeah, he was always encouraging
like to give more curiosity andyeah, how I find to derive this
(20:00):
research experience and researchmoment and if they helping for
interpretation and also like tryto think together.
And yeah, it was like, yeah,most of my good memory of, yeah,
biostat PhD was from himactually, yeah.
Rahul Ghosal (20:23):
hanks Emma for saying that, and I would like to
also like thank here the Annalsof Epidemiology, the editors,
the reviewers, the team and alsothe EPITalk team that we are
here with today for doing thiskind of outreach program, and I
think it's really important forscience to reach to people also
and to bring out the takeawaymessage of our research that we
(20:44):
are doing to a common person.
I think such programsdefinitely help.
So I thank Patrick and Sabrinafor organizing this and all the
team behind the EPITalk.
Patrick Sullivan (20:54):
Well, thank you.
Thank you both, and that bringsus to the end of this episode.
Thank you again to Dr.
Ghosal and to Ms.
Cho for joining us today.
It was such a pleasure to haveyou on the podcast.
Likewise, thank you, thank you.
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EPITalk, brought to you byAnnals of Epidemiology, the
(21:17):
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured on this episode andmore from the journal, you can
visit us online at www.
annalsofepidemiology.
org.
\\
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan,Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today we're talking about thearticle "Exploring the
(00:36):
Association Between DailyDistributional Patterns of
Physical Activity andCardiovascular Mortality Risk
Among Older Ad adults in NHANES2003 to 2006.
" And we're talking with two ofthe authors, Dr.
Rahul Ghosal and Ms.
Emma Cho, about this article.
You can find the full articleonline in the November 2024
(00:59):
issue of the journal at www.
annalsofepidemiology.
org.
I'm going to give you a briefintroduction to our guests.
Dr.
Rahul Ghosal is an assistantprofessor of biostatistics in
the Department of Epidemiologyand Biostatistics University of
South Carolina.
His research focuses ondeveloping novel statistical
(01:20):
methods such as functional dataanalysis with applications in
biosciences and alsodistributional data analysis for
modeling, wearable data withapplications in gait aging and
Alzheimer's disease.
Sunwoo Emma Cho is currently aPhD student at the Darla Moore
School of Business University ofSouth Carolina.
(01:40):
Previously, she was enrolled inthe biostatistics PhD program
at the same university.
She holds a bachelor's degreein econometrics and operations
research from Erasmus University, Rotterdam and a master's
degree in information systemsfrom Seoul National University.
Rahul and Emma welcome.
Rahul Ghosal (02:01):
Hi Patrick, good morning.
It's a pleasure to be here andwe're looking forward to this
podcast and discussing thepapers and the details behind it
.
Emma Cho (02:11):
Yeah, thank you for inviting us, yeah.
Patrick Sullivan (02:14):
So, Rahul, we'll start with you.
What was the purpose of thisstudy?
What research questions wereyou really trying to get at?
Rahul Ghosal (02:20):
So we know that cardiovascular disease is one of
the leading causes of death,both worldwide and also in the
US.
So previously a lack ofphysical activity has been shown
to be a prominent risk factorfor CVD mortality.
So we all now monitor our stepsin smartphones and get those
steps in.
But now these traditionalmeasurements of physical
(02:43):
activity previously wasself-reported and based on
various summary level metrics,which had its own drawbacks.
But recent advances in wearabledevice studies provide
continuously monitored andobjectively measured physical
activity data and, making use ofmore advanced statistical
methods, we can have a betterunderstanding of the
(03:04):
implications of physicalactivity in the context of CVD
mortality.
We can do it by consideringboth the timing of the physical
activity and its dailycompositions.
So our research questionbasically was whether these
daily distributional patterns orcompositional patterns of
physical activity, areassociated with cardiovascular
mortality risk among the olderadults and independent of aging.
(03:28):
So that's the research questionwe are trying to answer here.
Patrick Sullivan (03:31):
Great, so walk us through your study design,
then, and the methods that youused to conduct the research
that's reported in the article.
Rahul Ghosal (03:38):
Sure.
So for our study, what we usedwas the accelerated data from
the 2003-2006 National Healthand Nutrition Examination Survey
, also known as NHANES, and thisstudy actually provides
minute-by-minute levelaccelerometer data.
And we focused on the olderadults aged more than 50 years
(03:59):
and their physical activity datawas linked with mortality data
coming from National Death Index, and we censored the subjects
in December 2019 for collectingtheir mortality information.
And then we used this recentlydeveloped statistical methods in
functional and distributionaldata analysis, which is very
close to my research area, andwe obtained, based on this
(04:22):
minute-by-minute physicalactivity data, some temporarily
varying this minute by minutephysical activity data, some
temporally varyingdistributional patterns of
physical activity, and we callthese as L moments, and this can
be essentially thought as kindof robust analogs of traditional
moments like mean or standarddeviation.
But now, because we havemultiple days of data, we can
calculate them in a way suchthat they are varying over the
(04:45):
time of the day, so we cancollect various information,
such as the daily average, thevariability, the skewness and
their kurtosis patternthroughout the various times of
the day.
So now they kind of provide alot more information beyond the
daily average of physicalactivity, and these L moments
were then used as our mainexposure variables in a Cox
(05:08):
model to estimate the hazard ofcardiovascular mortality, while
adjusting for other knownconfounders such as gender, age,
smoking status and coronaryheart disease.
So that was basically themethod we used to answer our
research question.
Patrick Sullivan (05:24):
Great, and I'm going to turn to you for the
next question, and I wonder ifyou could tell us about what the
main findings from the studywas, and how did they align with
the hypotheses that you hadgoing into doing the research?
Emma Cho (05:36):
So in most of like association study, when we
explore relationship betweenphysical activity and CVD
mortality, those will usually gowith mean.
What we found from our study isthat it's not just the average
intensity of physical activityduring the day that matters, but
also the variability ofphysical activity, especially in
(05:59):
the afternoon, could also playa very crucial role to varying
the CVD mortality risk Also,interestingly, we also saw that
the association between dailymean, which is L1 moment, and
daily variability L2 moment ofphysical activity with CVD
mortality were much morepronounced on the weekdays and
(06:20):
it was not really significant onweekend.
So it was like, aligned withour research hypothesis, yeah,
which was surprising in a goodway, yeah.
So we think our studydemonstrated both circadian
rhythm of physical activity plusdaily decomposition can be
useful to design time of day andintensity specific physical
(06:41):
activity interventions toprotect against the CVD
mortality risk.
Patrick Sullivan (06:47):
Thank you.
So have others studied this?
It seems like a veryinteresting and a very specific
hypothesis, and a testable one,which is great.
But have others studied thissame kind of question, and how
do your findings compare withprevious literature on this
subject?
Rahul Ghosal (07:02):
Yeah, that's a very good question.
So our findings actually matchwith the previous literature in
terms of finding a protectiveeffect of physical activity
against the risk ofcardiovascular mortality.
But, as Emma mentioned, most ofthese previous studies were
based on various summary-levelmeasures of physical activity,
such as total activity count ormoderate to vigorous physical
(07:23):
activity, which is definitelyuseful.
However, our results now kindof complements those results and
also provide a deeperunderstanding into the
relationship between physicalactivity and cardiovascular
mortality risk.
By zooming into this physicalactivity pattern, because we
have continuous physicalactivity minute by minute level
(07:44):
we can, instead of using asingle summary level metric,
zoom it into much more andobtain these patterns of
physical activity varying overthe time of day, and so, by
considering its timing as wellas composition, we one of the
key takeaways is, even if youraverage physical activity is
(08:06):
same throughout the day, aperson with lower variability of
physical activity would have alower reserve of physical
activity and therefore would bean increased risk for
cardiovascular mortality.
So I guess the takeaway message,like our research would be like
it's not only the averagephysical activity that matters.
It's also good to havedifferent ranges of physical
(08:28):
activity and particularlydifferent times of the day.
Patrick Sullivan (08:30):
Right.
It seems like, just as a matterof you know, thinking about
some of the epi aspects of this,that one, just having the
objective device reduces someinformation bias, right, because
especially when we ask peopleabout physical activity, there
is a social desirabilitycomponent.
But then you have this kind ofhuge quantity of data that
really reflects much more.
(08:51):
And I wonder if you feel like,could you have gotten to the
issue of the variability withoutthat resolution of data?
If you're just asking peoplelike how many minutes do you
exercise a day, would you haveeven had the right data to ask
the question about thevariability?
Rahul Ghosal (09:09):
Right.
That's a very interestingquestion.
I think what helps in capturingthis variability, and
especially at various timepoints of the day, because we
have such multiple days ofcontinuously measured physical
activity.
I think- so therefore ourvariability estimates are much
more less uncertain.
We know in statistics it's notonly the point estimates that
(09:29):
matter, also the uncertainty ofthe estimate.
And because we have such hugelycollected, intensively
longitudinal data, we cancapture patterns like
variability or skewness withmuch less uncertainty.
So I think that's also one ofthe definite benefit of such
objectively measured data.
And another aspect is therecould be subjective bias
(09:50):
introduced.
People are trying to describethe variability in physical
activity, so I think definitelythat is also taken care of in
such objective assessments.
Patrick Sullivan (09:58):
Yeah.
Great.
Can you talk a little bit aboutsome of the strengths and any
important limitations to yourstudy?
Emma Cho (10:05):
Yeah, sure, I think already Dr.
Ghosal mentioned a lot oftrends and key insights from our
study.
Yeah, as he mentioned earlier,studies on the association
between physical activity andCVD mortality mainly relied on
summary scalar metrics such astotal activity count, or usually
we use survey data and we areasked by threshold-based measure
(10:29):
for specific activity likelight intensity, LIPA, moderate
to vigorous physical activity,and VPA.
But in our study we took it astep further by applying a novel
method, a partially functionaldistribution approach, to dive
deeper into the structural PA ina more detailed, granular way,
(10:51):
a temporally varyingdistribution patterns beyond the
mean.
So, yeah, key insight I thinkfrom our research is the
importance of variability inphysical activity, particularly
in the afternoon.
Yeah, we mentioned which can beleveraged to design more
effective PA interventions thataim at reducing CVD mortality,
which is a very significantproblem and public health
(11:13):
concern in the US and worldwide.
Patrick Sullivan (11:16):
Thanks for that information.
I wonder if you could also talka little bit about the
limitations of your study.
Emma Cho (11:21):
Yeah, so, likewise any study, our research also has a
few limitations.
First of all, the associationswe found between daily physical
activity patterns and the CBDmortality risk don't imply the
causal relationship.
Yeah, still, there could be apossible residual confounding
bias.
Second of all, since we use acause-specific Cox model,
(11:45):
participants who passed awayfrom causes other than CBD were
treated as right-sensor, butsome of those individuals might
have underlying cardiovascularconditions that we didn't
account for, which couldintroduce some kind of bias in
our estimates.
And lastly, yeah, there couldbe a further methodological work
(12:05):
that can more directlyincorporate survey-weighted
causal survival model to exploreas a further study.
Patrick Sullivan (12:19):
Great.
So now we're going to move onto a section of the podcast that
we call Behind the Paper, andwe really think it's important
to recognize that our methodsare analytical and our process
is scientific, but this work isultimately done by people, and
especially the relationshipsbetween co-authors and how we
work together.
So I just want to ask each ofyou a couple questions to
(12:40):
understand more about how youwork on this project together
and how that sort of fit intoyour mentoring relationship.
So, Emma, I'll start with youand just ask what's your
favorite experience been in yourPhD program so far?
Emma Cho (12:55):
Uh, so far- actually, my initial goal to join the
biostat program was likedeveloping, apply the
computational method thateffectively bridge medicine or
probably carries overstatistical or data science
methodologies.
Yes, through this program I'vebeen able to explore new
methodologies, especially forthis study.
(13:16):
Yeah, we can see more deep ofthe new data structure, new data
frame with new methodologicalstudies.
So, yeah, it was mostfascinating part.
I was in the PhD program andthen I worked as a hospital
(13:37):
researcher for a while, so I sawall the moments that come
together and then, yeah, Irealized I can develop further
study based on the program Ilearned.
Patrick Sullivan (13:50):
Yeah, those moments when all the pieces come
together are like the days thatwe work for for sure.
Is there anything that you wishyou knew before you started the
PhD program?
Emma Cho (14:00):
Yeah, actually I was interested in a lot of different
sources and data structure.
Also, like because usually ifwe when we like dealing with the
table data, it does not giveall information.
So I was interested in otherobjective measures such as like
variables or so image and textand yeah, those all new
(14:23):
methodology curve yeah, that wasI think it was.
I want to dig into, know moreabout.
Like to improve better medicineresearch.
Patrick Sullivan (14:34):
Great.
Okay, and Rahul, we're going toturn back to you now for
another question, so I'm goingto call you Dr.
Ghosal for this one, becauseI'm asking you like, in your
role working with Emma as heradvisor, what was that
experience like and what advicedo you have for first time
mentors who may be working withstudents about how to enter into
that mentoring relationship?
(14:56):
So actually Emma was kind of myfirst student when she started
working with me and I franklyhad a great time working with
Emma.
She's like an excellent listenerand also has a natural kind of
scientific curiosity as aresearcher, apart from her
excellent skills in coding.
So I really enjoyed working withher, like our meetings, and the
(15:18):
project eventually turned outto be a lot of fun and actually
we are also continuing ourcollaboration and exploring some
interesting new directionsbased on the findings we've seen
in this study.
So definitely I had a very goodexperience working with her and
(15:41):
regarding my advice tofirst-time mentors, it would be
to have a good balance betweenbeing hands-on and also giving
the students their own space fortheir own independent research
and exploration.
And we can all probably kind ofuse our personal experience
because it was not so long agothat we were students ourselves,
so we can identify that, whataspects we needed most from our
(16:02):
mentors, and we can apply thosein our day-to-day dealings or
the meetings with the students.
Also, it helps to understandthe motivation, the goals of the
student and connect with themnon-academically too, and when
you are a mentor and I have seenthat worked a great bit in my
experience working with thestudents.
Great.
So thanks for sharing that fromboth your perspectives about
(16:24):
just how it was to work together.
I'm also really alwaysinterested in how the work is
done on these papers, so Iwondered if you could say a
little bit about what roles eachof you played in the like the
conception, the analysis, thewriting, the submitting.
How did you divvy up that work?
Rahul Ghosal (16:43):
So actually we had a great team behind this one.
Emma was obviously the drivingforce behind the study and she
contributed in the research interms of formal investigation,
doing all the analysis,validation, visualization and
software and also writing.
But also behind this we hadsome amazing colleagues of mine
(17:04):
working.
I should mention the name of Dr.
Marcos Matabuena.
So he is currently a postdoc inHarvard and expert in this
functional data analysis areaand he has a lot of experience
working with this enhanced data.
So he was kind of our go-to guy, particularly in terms of the
data aspects of it.
Dr.
Jingkai Wei, who is anassistant professor currently at
(17:26):
the University of Texas HealthScience Center at Houston.
So he's an expert incardiovascular disease
epidemiology, cognitive agingand dementia.
So he was kind of the domainexpert and the epidemiology
expert in this study and he kindof definitely helped us in the
conceptualization and thewriting aspect of the study.
And finally, Dr.
(17:46):
Enakshi Saha is also currentlyassistant professor at the
University of South Carolina.
She is an expert in Bayesianstatistical methods and
functional data analysis andmachine learning.
So she also helped in theconceptualization, the writing
part and the supervision of theproject.
And whenever I needed myco-authors they were there for
me and, like, helped as the bestto their capacity.
(18:09):
So I think we had, as Imentioned, a great team on this
one, and I think that is howgood science should be done
working with a great team.
So definitely I'm grateful toall of them.
Patrick Sullivan (18:23):
It's definitely the best science and
I think it's more fun and justsort of professionally
fulfilling, like in two ways.
One, to interact withcolleagues, but also that I
think the best research rarelysits in one person's domain of
expertise, and so when you talkabout what each person brings
and sort of crossing over,sometimes you get into an
analysis and then you realizelike, oh, there's a type of data
here that I am not quite surehow to handle, and so then
(18:44):
finding that colleague whosewhole thing is handling that
kind of data.
I do think that, as much as wemake an analysis plan, those
things have to evolve, and sosometimes the authorship list
may also either grow or you know.
Rahul Ghosal (18:58):
Definitely, definitely.
Yeah, I agree.
And sometimes, yeah, itdefinitely helps to have like
experts at their respectivefields working with you.
And definitely yeah, whileworking on this project, I also
began to appreciate like being apart of a team and working on
to solve a common researchquestion.
I think that's really powerful.
So I really enjoy when workingthis type of projects now where
(19:21):
I can bring some of mymethodological work to solve
like interesting scientificanswer, scientific questions,
and definitely looking forwardto doing much more of it in
future.
Patrick Sullivan (19:30):
Great.
So Dr.
Ghosal, Ms.
Cho, do either of you have haveanything else you'd like to
share with our listeners?
Emma Cho (19:38):
Yeah, I think I forgot like one of important, my
enjoyable moment from my PhD.
Actually, it was Dr.
Ghosal, yeah, yeah, becauseyeah, he was always encouraging
like to give more curiosity andyeah, how I find to derive this
(20:00):
research experience and researchmoment and if they helping for
interpretation and also like tryto think together.
And yeah, it was like, yeah,most of my good memory of, yeah,
biostat PhD was from himactually, yeah.
Rahul Ghosal (20:23):
hanks Emma for saying that, and I would like to
also like thank here the Annalsof Epidemiology, the editors,
the reviewers, the team and alsothe EPITalk team that we are
here with today for doing thiskind of outreach program, and I
think it's really important forscience to reach to people also
and to bring out the takeawaymessage of our research that we
(20:44):
are doing to a common person.
I think such programsdefinitely help.
So I thank Patrick and Sabrinafor organizing this and all the
team behind the EPITalk.
Patrick Sullivan (20:54):
Well, thank you.
Thank you both, and that bringsus to the end of this episode.
Thank you again to Dr.
Ghosal and to Ms.
Cho for joining us today.
It was such a pleasure to haveyou on the podcast.
Likewise, thank you, thank you.
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
EPITalk, brought to you byAnnals of Epidemiology, the
(21:17):
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured on this episode andmore from the journal, you can
visit us online at www.
annalsofepidemiology.
org.
\\
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