Live Webinar with Dr. Grace Lo

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Welcome everyone. Thank you for joining

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the OAI core knowledge bases oi.edu live

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webinar. Today we are joined by Dr.

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Grace Lo who is an associate professor

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at Baylor College of Medicine. The title

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of today's talk is um lifetime leisure

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physical activity questionnaire in the

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osteoarthritis initiative. And following

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the presentation we should have a few

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minutes for Q&A. If you have any

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questions during the meeting, feel free

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to type them into the chat at any point

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and we'll try to get them at the end.

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Without further ado, I will let Dr.

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Grace Dr. Lo take over.

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Thank you, Julieann. Um, and thank you

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everybody for uh joining us for this

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webinar. So, as Julieann um said, I'm

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going to be talking about the lifetime

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leisure physical activity questionnaire,

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which um is included within the

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osteoarthritis initiative.

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So my objectives for today are to talk

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about how this data was acquired, how to

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access it now, what can be done with it,

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what has been done, and analytic

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strategies to avoid.

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So first to talk about how the data was

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acquired as part of my K23 that I I got

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funded on um a few years ago, I was able

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to add this questionnaire into the OAI.

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Um, at the time of the funding of the

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project, the OAI had already started the

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96-month visit, so I wasn't able to

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capture everybody, but as soon as the

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questionnaire was approved by the OAI

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coordinating center and the OAI site

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PII, then it was administered.

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So the questionnaire that we put in was

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um basically modeled after this

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historical physical activity um

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instrument that was initially created by

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um Andrea Kriska and published in the

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American Journal of Epidemiology in

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1988. And this questionnaire was

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originally designed to evaluate the

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relationship between physical activity

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and bone health or osteoporosis.

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At that time it was orally administered

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and when we were talking about it within

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the OAI it was felt like the

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administration of it in that manner

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would be too burdensome and so what we

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did was we converted it to a

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self-administered format and this has

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also been previously done by this other

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author also published in the same

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journal um but later in 2002.

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So what we did was we incorporated our

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questionnaire into the take-home

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questionnaire that was sent home prior

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to the 96-month visit and we asked

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participants to complete that before

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their in-person visit and they brought

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that questionnaire to that visit and

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then the OAI staff would check for

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completion of the questionnaire and if

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it wasn't completed they would assist

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the participants in finishing it.

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So where is this data now? So uh the OAI

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knowledge base is all about helping

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people to understand how to get this

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data. So I thought it'd be helpful to

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kind of walk through exactly how we can

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look at this information and um the this

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particular data is a little complicated.

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So I do think that um understanding this

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process will help you understand how to

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look at many other types of data as

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well.

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So the data for the OAI um for the

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clinical and the radiologic imaging is

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stored at this particular um URL. It's

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hosted by the NDA and if you use this QR

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code that will also take you there.

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So this is what this website looks like.

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Um and it hosts many other um data sets

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as well, but ours is the OAI. So you

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would just click up here and this would

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take you to the Osteoarthritis

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initiative. you do have to create an

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account, which actually doesn't take

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very long. Um, except for the login.gov

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part, which is painful on many levels.

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I'm sure everybody knows about that.

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But, um, anyway, once you have an

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account, then you can have access to

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this. But, um, when you get to this

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page, this is the, uh, tab that I think

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is, uh, most useful. So, you would click

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on this. And I know the kneejerk

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reaction would be to want to go straight

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to um downloading the complete data

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sets, but I'm actually encouraging that

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you um take a step before that and to

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look at the download documentation

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um because I think this actually makes

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it much clearer how to interpret the

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variables that are available to you.

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So when you click on that, this will

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take you to a bunch of options, but this

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is the one that I'm I'm advocating for

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is that you look at the annotated

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questionnaires. Um, and so you will

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click on this and that will take you to

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this compressed zip file and then you

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can unzip it and then you have all these

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options. And I'm not saying that you

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have to download all of them, but it

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actually does download super fast. Um,

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but the one of relevance for the

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lifetime leisure um uh physical activity

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questionnaire is is this one, the

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96-month take-home questionnaire. So, um

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the good news is that the labeling for

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the um for the files is actually very

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intuitive and I think you can kind of

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figure out which ones you need. So, um

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we'll take a look at this one.

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So, um I'm not going to show you that

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entire file, but what I did was I took

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us to where the physical activity

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questionnaire is. And um I do think it's

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very important that you understand what

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was in the questionnaire that people

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read prior to actually completing the

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questionnaire because it does help

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dictate um understanding for how they

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answered what they answered. So, I'm not

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going to read this whole thing to you,

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but the main gist here is what is

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underlined here is that we looked at

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people um or we asked people to really

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think about the activities that they did

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over a lifetime, but to only consider

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activities that they did for at least 20

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20 minutes within a given day. And it

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they didn't have to be consecutive

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minutes. They could be broken up over a

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day. And they had to do it at least 10

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times in their lives. Um and they were

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also asked to include activities done

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during a physical education class.

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So this is the beginning part of the

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list. So there in total were 37

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activities that we asked about and this

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is alphabetized. This is not based on

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you know sort of an activity we're more

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interested in. Just to keep it simple we

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um did listed in alphabetical order. So

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the activities are listed here and then

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the age ranges are listed here. So what

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we did was we broke it down into um four

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different age ranges. 12 to 18, 19 to

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34, uh 35 to 49, and then um ages 50 and

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older. So then what we had them do at

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this part was actually very simple. Just

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mark in the little bubble if they

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actually did the activities during those

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age ranges. Um and it had to meet the

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criteria that we just talked about

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before.

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So, these are um the full 37 activities.

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Um and uh and you don't have to like

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take a picture of them because you

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always download this file by yourself on

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your own and look at it as much as you

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like. But these were activities that um

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I was interested in or that were on a

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prior questionnaire um and uh we thought

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were of particular re relevance. Okay.

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So then after people completed this,

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then we got to the next part of the

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questionnaire, which is question 37. And

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what we did was we wanted to get more

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granular information um as far as the

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amount that people participated in the

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activities. So first here we're looking

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at ages 12 to 18. And what we wanted to

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do was to ask people to look and think

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about the top three activities that they

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participated in during that age range.

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And then they would mark down the

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activity code based on what we um showed

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on the previous 37. They would list the

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activity code here and they would write

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down the activity that it uh uh

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represented and they would do that for

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um as I said for the top three

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activities.

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Then, okay, this looks really busy, but

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um if you know that question 37 came

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here, like everything that you wrote

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over there and question 37 just came

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straight right here. And what we're

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doing then is we're trying to get more

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information about each of the individual

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activities that they listed on the prior

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page. Right? So, we have question A, B,

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C, and D. And here are the answers,

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right? A, B, C, and D. So what we did

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was we wanted to know how many years in

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that age range between 12 and 18 did

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they participate in the activity. And so

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we broke it down in this way so that

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people didn't have to think too hard

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about which one um they had to pick. So

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they didn't have to be super exact. Um

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and we did this for all of the questions

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here. So we looked at the number of

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years that people participated in the

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activity and then we looked at the

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number of months per year they

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participated and then the number of

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times per month. And this allowed us to

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come up with a a quasi mathematical

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equation of how many times people

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participated in an activity during that

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time frame. And then we asked people to

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just answer if they participated in the

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activity competitively. And this was not

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anything that we um gave any specific

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directions on just what we have listed

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here. Um we specifically said actually

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it does not mean that you have to have

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participated in organized competitions

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but that you participated on a

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competitive level. So this was

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self-administered. People just kind of

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took that for what it was.

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Um so after people answered all of these

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questions for the first activity they

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would also do it for the second and the

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third activity similarly.

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So then the next question, we still

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looked at the same age range of um 12 to

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18, but then we asked about walking for

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exercise because we were particularly

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interested in that. So so you do have to

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bear in mind that when we look at

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walking, it's not necessarily listed as

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a top three activity. Um but we did ask

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similar questions um related to if they

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did it or not and then how many years

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they did it during that time frame, how

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many months per year and then times per

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month. And again, this allowed a a way

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to quantitate the amount that people

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participated in the activity.

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So, I just walked through all that part

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of the questionnaire and then um I just

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wanted you to understand the way that

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the questionnaire was laid out. But then

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I want you now to understand that there

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is grayed out lettering of the variables

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that are used to store the responses. So

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this is really important because the way

0:10:43.839,0:10:48.560
that this document is most useful is

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that if you can understand which

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variables are associated with which

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questions. So just I just pulled one of

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the questionnaires um back what you see

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here. So right here you can see that

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there's this grayed out thing that's

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really hard to read. So I'm circling it

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for us here. And you can see this says

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AC1

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A R1. So if you're looking for this

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particular variable in the data set,

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what you'll do is you'll look at the all

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clinical 10 data set or I mean actually

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I'll get to that in a minute I think. Um

0:11:19.440,0:11:24.959
oh no I think I already said that

0:11:21.040,0:11:28.800
anyway. So but this is at visit 10. So

0:11:24.959,0:11:31.040
V10 which is the 96-month visit and so

0:11:28.800,0:11:35.279
the variable name within the all

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clinical 10 data set will be V10

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AC1 A R1 and then you'll know that that

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is what the activity code is for this

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first activity and then if you look

0:11:44.480,0:11:52.079
closely there are lots of other gradeout

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variables and this is how you can um use

0:11:52.079,0:11:57.600
the data when you write your own program

0:11:54.560,0:12:00.079
or your programmer writes it for you. Um

0:11:57.600,0:12:03.680
but it's important to see all these um

0:12:00.079,0:12:06.320
gray notations to know how to um access

0:12:03.680,0:12:09.360
the data.

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Um oh okay so this this part is the

0:12:09.360,0:12:12.959
whole clinical part. So now we can get

0:12:11.040,0:12:14.959
to downloading the complete data sets.

0:12:12.959,0:12:17.279
And so I got ahead of myself a little

0:12:14.959,0:12:19.600
bit but um what you would do here is

0:12:17.279,0:12:22.000
that you would click this button and

0:12:19.600,0:12:24.880
that would take you to this menu here.

0:12:22.000,0:12:27.519
And I recommend using this all clinical

0:12:24.880,0:12:30.320
data set. So I personally use SAS for

0:12:27.519,0:12:32.800
programming. So this is the one that I

0:12:30.320,0:12:35.120
would choose. And as I mentioned um a

0:12:32.800,0:12:38.720
little early before is that the data set

0:12:35.120,0:12:41.440
of relevance is the all clinical 10 data

0:12:38.720,0:12:43.200
set. That's where the um data for the

0:12:41.440,0:12:45.760
lifetime leisure physical activity is

0:12:43.200,0:12:48.399
located.

0:12:45.760,0:12:51.839
Okay. So now that I've talked about what

0:12:48.399,0:12:54.160
it looks like um what can be done with

0:12:51.839,0:12:55.920
the data. So uh in the time that I've

0:12:54.160,0:12:57.600
used this data there are two general

0:12:55.920,0:12:59.760
strategies that make sense to me that I

0:12:57.600,0:13:02.480
have personally used. So the first

0:12:59.760,0:13:06.000
strategy really looks at the exposures

0:13:02.480,0:13:09.680
at different lifetimes in sorry certain

0:13:06.000,0:13:13.360
times in life um in those four different

0:13:09.680,0:13:15.200
age ranges or cumulatively over time. Um

0:13:13.360,0:13:16.959
and then look at the associations with

0:13:15.200,0:13:19.839
the outcomes in the OAI

0:13:16.959,0:13:22.320
cross-sectionally. Um,

0:13:19.839,0:13:25.360
and when we're doing this, we're

0:13:22.320,0:13:28.000
basically using the outcomes uh

0:13:25.360,0:13:31.120
cross-sectionally as surrogates for a

0:13:28.000,0:13:32.959
cumulative incidence of uh disease and

0:13:31.120,0:13:35.920
symptoms. So, uh obviously for

0:13:32.959,0:13:39.680
neosticarthritis.

0:13:35.920,0:13:41.519
Okay. So um I showed this as an example

0:13:39.680,0:13:43.920
because I think it's easier if we talk

0:13:41.519,0:13:46.800
about a specific

0:13:43.920,0:13:49.519
project that we looked at uh this data

0:13:46.800,0:13:50.959
to understand what I'm talking about. So

0:13:49.519,0:13:53.040
this was the first paper that we

0:13:50.959,0:13:55.200
published looking at the association

0:13:53.040,0:13:59.040
between running and neostarthritis that

0:13:55.200,0:14:04.399
was published in um ACNR in 2017

0:13:59.040,0:14:07.279
um nine years ago. Um anyway um so in

0:14:04.399,0:14:10.240
this table this really represents what I

0:14:07.279,0:14:13.839
was suggesting on the prior um slide in

0:14:10.240,0:14:15.760
terms of the analytic strategy. So what

0:14:13.839,0:14:17.600
we look at and so although this is kind

0:14:15.760,0:14:21.040
of busy I think it is important to look

0:14:17.600,0:14:24.240
at the um whole table in this first

0:14:21.040,0:14:28.240
group of uh analyses in this top row

0:14:24.240,0:14:30.800
we're looking at running over a lifetime

0:14:28.240,0:14:32.240
and then um below that we look at the

0:14:30.800,0:14:36.720
different age ranges. So we're looking

0:14:32.240,0:14:40.480
at ages 12 to 18 and then ages 19 to 34

0:14:36.720,0:14:43.600
ages 13 sorry 35 to 49 and then ages 50

0:14:40.480,0:14:47.120
and older. Um and and really the

0:14:43.600,0:14:50.639
arrangement of the uh data here is the

0:14:47.120,0:14:54.160
same in each age range except that the

0:14:50.639,0:14:56.160
age range of interest is different. So

0:14:54.160,0:14:59.120
um then I thought we would look at this

0:14:56.160,0:15:02.399
top row and I'd walk you through that.

0:14:59.120,0:15:04.720
So what we have here is um looking at

0:15:02.399,0:15:06.880
lifetime running. And so what we did was

0:15:04.720,0:15:08.880
we compared people who were runners to

0:15:06.880,0:15:11.360
those who were non-runners.

0:15:08.880,0:15:14.160
And we looked at the prevalence of

0:15:11.360,0:15:16.720
frequent knee pain. And what you see is

0:15:14.160,0:15:18.880
that um the people who were runners were

0:15:16.720,0:15:21.120
less likely to have frequent knee pain

0:15:18.880,0:15:23.760
compared to the non-runners. And this

0:15:21.120,0:15:26.160
resulted in an unadjusted odds ratio of

0:15:23.760,0:15:28.959
78. And this was statistically

0:15:26.160,0:15:32.800
significant. And when we adjusted for

0:15:28.959,0:15:35.760
age, sex, BMI, and other activities that

0:15:32.800,0:15:38.959
also associated with running, the U

0:15:35.760,0:15:41.760
finding was robust and it still remains

0:15:38.959,0:15:44.800
statistically significant.

0:15:41.760,0:15:47.760
Um, so after we looked at this um

0:15:44.800,0:15:50.639
relationship dichomously, then we looked

0:15:47.760,0:15:52.480
um at a dose response. So what we did

0:15:50.639,0:15:54.800
was we took all that information that we

0:15:52.480,0:15:56.560
talked about earlier that allowed us to

0:15:54.800,0:15:59.120
know the number of times that people

0:15:56.560,0:16:00.880
participated in the activity um and then

0:15:59.120,0:16:02.399
we broke them down into different groups

0:16:00.880,0:16:05.279
based on the number of times they

0:16:02.399,0:16:07.519
participated. So um the people who are

0:16:05.279,0:16:11.360
in this high group participated in

0:16:07.519,0:16:12.800
running the most often. Um, and so what

0:16:11.360,0:16:15.440
we see is that the prevalence of

0:16:12.800,0:16:18.160
frequent knee pain is the lowest in the

0:16:15.440,0:16:20.480
people who ran the most. And this

0:16:18.160,0:16:23.519
resulted in an unadjusted odds ratio

0:16:20.480,0:16:25.600
that um is 65. And this was

0:16:23.519,0:16:29.199
statistically significant. And this was

0:16:25.600,0:16:31.759
also um stayed um statistically

0:16:29.199,0:16:35.600
significant despite adjusting for age,

0:16:31.759,0:16:39.360
sex, BMI, and other activities that are

0:16:35.600,0:16:41.440
um associated with running.

0:16:39.360,0:16:43.759
Um and then we looked at the other

0:16:41.440,0:16:45.519
outcomes of interest. So basically this

0:16:43.759,0:16:47.120
table is the same arrangement but now

0:16:45.519,0:16:50.880
we're looking at radiographic evidence

0:16:47.120,0:16:53.360
of a disease. And um in this instance um

0:16:50.880,0:16:55.279
we were able to see that uh there was

0:16:53.360,0:16:58.160
actually less radiographic evidence of

0:16:55.279,0:16:59.920
osteoarthritis. Um and but this only

0:16:58.160,0:17:02.959
remained statistically significant in

0:16:59.920,0:17:04.880
the unadjusted odds ratio but for the

0:17:02.959,0:17:06.959
adjusted this was no longer significant.

0:17:04.880,0:17:09.839
So, you know, in our first group of

0:17:06.959,0:17:11.520
analyses, we included BMI as um one of

0:17:09.839,0:17:13.120
the coariantss that we adjusted for.

0:17:11.520,0:17:15.199
There's a lot of controversy on whether

0:17:13.120,0:17:17.280
or not we should include BMI as a

0:17:15.199,0:17:20.880
coariant because if it's on the causal

0:17:17.280,0:17:22.799
pathway, then maybe we shouldn't. So, um

0:17:20.880,0:17:24.240
anyway, uh but I think that it's

0:17:22.799,0:17:27.199
important to understand that when you're

0:17:24.240,0:17:30.400
you're interpreting the data here. And

0:17:27.199,0:17:33.120
then we also then looked at um the

0:17:30.400,0:17:36.000
relationship of these exposures with the

0:17:33.120,0:17:37.840
combined outcome of both symptoms being

0:17:36.000,0:17:39.440
frequent knee pain and radiographic

0:17:37.840,0:17:42.480
evidence of disease within the same

0:17:39.440,0:17:45.840
knee. And we saw a similar arrangement.

0:17:42.480,0:17:47.360
Um and in this instance again um it was

0:17:45.840,0:17:50.480
statistically significant in this

0:17:47.360,0:17:53.520
highest group um that uh people were

0:17:50.480,0:17:55.280
less likely to have this outcome um if

0:17:53.520,0:17:57.520
they were in this high group of running

0:17:55.280,0:17:59.600
or even actually just dichomously but

0:17:57.520,0:18:01.520
once you adjusted for all of the

0:17:59.600,0:18:05.120
covariants then it was no longer

0:18:01.520,0:18:08.160
statistically significant.

0:18:05.120,0:18:10.720
So um so the strengths of this approach

0:18:08.160,0:18:13.280
um include that uh the osteoarthritis

0:18:10.720,0:18:15.440
initiative has the only data set that

0:18:13.280,0:18:18.640
we're aware of that has information on

0:18:15.440,0:18:21.360
lifetime leisure physical activity uh on

0:18:18.640,0:18:22.960
people who are not selected based on

0:18:21.360,0:18:24.880
their participation in those activities.

0:18:22.960,0:18:26.880
And so this allows us to really look at

0:18:24.880,0:18:29.200
people who participate in these

0:18:26.880,0:18:32.000
activities on a much more recreational

0:18:29.200,0:18:34.480
level as opposed to much of the um

0:18:32.000,0:18:36.160
literature that exists really focuses on

0:18:34.480,0:18:38.480
people who participate in these

0:18:36.160,0:18:40.799
activities at an elite level. they like

0:18:38.480,0:18:44.720
they look at people who are Olympic

0:18:40.799,0:18:48.000
athletes in these different um uh sports

0:18:44.720,0:18:51.039
and um that's less pertinent to the

0:18:48.000,0:18:55.039
everyday person who is um going for a

0:18:51.039,0:18:57.120
run or you know going bike grading. So I

0:18:55.039,0:18:59.600
do think this is important in terms of

0:18:57.120,0:19:04.000
generalizability of the studies that

0:18:59.600,0:19:06.400
we're um conducting. Um and uh the other

0:19:04.000,0:19:08.400
strength of this study is that we really

0:19:06.400,0:19:10.880
have very highly standardized measures

0:19:08.400,0:19:13.120
of osteoarthritis. So the x-rays are

0:19:10.880,0:19:15.440
very um standard in the way that they're

0:19:13.120,0:19:18.400
acquired and the way that they're read.

0:19:15.440,0:19:22.720
The questions for um symptoms are also

0:19:18.400,0:19:26.960
very systematic um and um have a lot of

0:19:22.720,0:19:28.480
historical use within osteoarthritis.

0:19:26.960,0:19:30.559
So those are the strengths but there are

0:19:28.480,0:19:32.480
limitations to this approach. So um one

0:19:30.559,0:19:35.039
of the uh limitations is that we were

0:19:32.480,0:19:37.200
not able to ascertain the entire cohort

0:19:35.039,0:19:39.679
um within the OI given the timing of the

0:19:37.200,0:19:42.320
funding of the project. So um the

0:19:39.679,0:19:44.880
96-month visit had already begun by the

0:19:42.320,0:19:46.880
time that um we were able to administer

0:19:44.880,0:19:49.360
this questionnaire. So um we didn't

0:19:46.880,0:19:51.679
catch everybody but we still got um more

0:19:49.360,0:19:53.520
than 2600 people and that's still a

0:19:51.679,0:19:56.400
substantial number of participants um

0:19:53.520,0:19:58.640
who we do have data on.

0:19:56.400,0:20:01.679
And the other important limitation is

0:19:58.640,0:20:04.160
that the time po time point um that we

0:20:01.679,0:20:06.960
have the most central readings of

0:20:04.160,0:20:08.640
radioraphs um that was approximate to

0:20:06.960,0:20:10.480
the historical physical activity

0:20:08.640,0:20:13.039
questionnaire administration which was

0:20:10.480,0:20:15.760
the 96-month visit is the 48-month

0:20:13.039,0:20:17.440
visit. So ideally we would have liked

0:20:15.760,0:20:20.640
you know the exposure and the outcome to

0:20:17.440,0:20:23.120
be at the same visit but um it's just

0:20:20.640,0:20:26.080
not what it is. Um so they're not

0:20:23.120,0:20:28.880
contemporaneous. It's not ideal, but um

0:20:26.080,0:20:30.720
as in many um cohort studies, this kind

0:20:28.880,0:20:32.640
of thing does happen. And so I think we

0:20:30.720,0:20:35.679
just have to accept it for what it is.

0:20:32.640,0:20:37.440
Um there are no other data sets really

0:20:35.679,0:20:40.000
that I'm aware of that even has this

0:20:37.440,0:20:43.280
level of data. So um it's a good

0:20:40.000,0:20:48.720
starting point from which to produce

0:20:43.280,0:20:51.280
some data to to um provide a support for

0:20:48.720,0:20:52.640
uh additional data that needs to be or

0:20:51.280,0:20:55.039
additional studies that need to be

0:20:52.640,0:20:57.200
conducted down the line to further

0:20:55.039,0:21:00.799
understand the relationship between

0:20:57.200,0:21:02.400
these uh activities and then also um

0:21:00.799,0:21:07.760
nesteoarthritis.

0:21:02.400,0:21:11.360
So um so despite that um the time point

0:21:07.760,0:21:12.880
uh for the uh outcome measures and the

0:21:11.360,0:21:15.280
um the questionnaire are not

0:21:12.880,0:21:17.200
contemporaneous uh our participants were

0:21:15.280,0:21:19.039
not aware of our hypothesis and so they

0:21:17.200,0:21:20.960
were unlikely to be biased in their

0:21:19.039,0:21:22.400
responses on the physical activity

0:21:20.960,0:21:24.880
questionnaire. So I do think that that

0:21:22.400,0:21:27.440
is helpful.

0:21:24.880,0:21:30.240
So okay so that's one approach to how to

0:21:27.440,0:21:32.159
look at the data. Um, and so the second

0:21:30.240,0:21:34.240
approach is a little different. And so

0:21:32.159,0:21:37.520
instead of using all of the data from

0:21:34.240,0:21:41.120
the exposure, we really only look at um

0:21:37.520,0:21:42.799
at ages 50 and over. So the point or the

0:21:41.120,0:21:44.880
purpose of looking at that time frame is

0:21:42.799,0:21:47.840
that that's around the age that people

0:21:44.880,0:21:49.919
were enrolled into the OAI. And so

0:21:47.840,0:21:52.240
looking at that exposure will help us to

0:21:49.919,0:21:53.760
understand um what their exposure is

0:21:52.240,0:21:55.440
during the time frame that they were

0:21:53.760,0:21:59.799
followed within the OAI. So this will

0:21:55.440,0:21:59.799
allow some longitudinal evaluations.

0:22:00.159,0:22:04.400
So again, I'm going to just walk through

0:22:01.760,0:22:06.159
our second paper that we published that

0:22:04.400,0:22:10.799
use this particular strategy. And again,

0:22:06.159,0:22:12.320
we were looking at running um and so um

0:22:10.799,0:22:13.600
because this is a longitudinal

0:22:12.320,0:22:15.280
evaluation now, not just

0:22:13.600,0:22:17.760
cross-sectional, what we're looking at

0:22:15.280,0:22:19.039
is from baseline to the follow-up visit,

0:22:17.760,0:22:21.200
which we have the most amount of

0:22:19.039,0:22:23.840
information on. So that's the baseline

0:22:21.200,0:22:26.640
to the 48-month visit. we look at these

0:22:23.840,0:22:28.080
different outcomes of interest. So, um

0:22:26.640,0:22:30.640
in this instance, we were looking at

0:22:28.080,0:22:33.440
Kilgrren and Lawrence worsening and when

0:22:30.640,0:22:35.679
we compared runners to non-runners, um

0:22:33.440,0:22:39.360
we saw that the outcomes were about the

0:22:35.679,0:22:41.039
same. Um and this resulted in unadjusted

0:22:39.360,0:22:43.919
and adjusted odds ratios that were not

0:22:41.039,0:22:45.919
statistically significant. Um and you'll

0:22:43.919,0:22:47.520
notice that uh we only did dichomous

0:22:45.919,0:22:49.600
outcomes here because the amount of

0:22:47.520,0:22:51.120
participation of running in this

0:22:49.600,0:22:52.480
particular group was smaller. So you

0:22:51.120,0:22:54.000
know you can really only do this like

0:22:52.480,0:22:55.120
breaking out into smaller groups if

0:22:54.000,0:22:57.039
there are enough people who are

0:22:55.120,0:22:59.919
participating in the particular activity

0:22:57.039,0:23:02.799
at the age range of interest.

0:22:59.919,0:23:05.600
Um then we looked at medial joint space

0:23:02.799,0:23:08.880
narrowing worsening and this was uh also

0:23:05.600,0:23:10.480
about the same same it was not um the

0:23:08.880,0:23:12.799
odds ratios were not statistically

0:23:10.480,0:23:14.480
significant. And then when we looked at

0:23:12.799,0:23:16.320
the development of frequent knee pain

0:23:14.480,0:23:18.960
and so in this instance it's important

0:23:16.320,0:23:21.360
to understand um that in order to

0:23:18.960,0:23:23.039
develop frequent knee pain then you had

0:23:21.360,0:23:25.200
to be free of frequent knee pain at

0:23:23.039,0:23:26.799
their baseline visit and then you would

0:23:25.200,0:23:29.200
have developed frequent knee pain at the

0:23:26.799,0:23:31.679
48-month visit. And what we see is that

0:23:29.200,0:23:34.880
this um prevalence was also about the

0:23:31.679,0:23:37.120
same and again the odds ratios were not

0:23:34.880,0:23:38.480
statistically significant. So I think

0:23:37.120,0:23:41.520
this is actually the most interesting

0:23:38.480,0:23:44.640
part of this particular um analysis is

0:23:41.520,0:23:46.159
that um in this analysis where there was

0:23:44.640,0:23:47.840
improvement of frequent knee pain. So

0:23:46.159,0:23:50.799
that means that at their baseline visit

0:23:47.840,0:23:53.360
they had to have uh answered affirmative

0:23:50.799,0:23:56.320
to the frequent knee pain question. Then

0:23:53.360,0:23:59.280
by the 48-month visit they had answered

0:23:56.320,0:24:01.440
no to the frequent knee pain question.

0:23:59.280,0:24:04.720
um the people who were runners were far

0:24:01.440,0:24:06.559
more likely to answer or to have this

0:24:04.720,0:24:09.280
particular outcome compared to the

0:24:06.559,0:24:12.000
non-runners. Um and this resulted in an

0:24:09.280,0:24:13.440
unadjusted odds ratio of 1.6, meaning

0:24:12.000,0:24:14.880
that runners were more likely to have

0:24:13.440,0:24:16.880
this arrangement compared to

0:24:14.880,0:24:18.720
non-runners. And this was uh

0:24:16.880,0:24:21.360
statistically significant even in the

0:24:18.720,0:24:22.799
adjusted models.

0:24:21.360,0:24:24.480
So this was actually re really

0:24:22.799,0:24:26.559
reassuring to people. There's always

0:24:24.480,0:24:28.720
been this concern about running being

0:24:26.559,0:24:32.320
harmful to people with osteoarthritis.

0:24:28.720,0:24:35.200
And so this really um

0:24:32.320,0:24:37.120
helped to allay people's concern that um

0:24:35.200,0:24:38.799
that it did not appear harmful and it

0:24:37.120,0:24:40.480
could even be potentially beneficial

0:24:38.799,0:24:42.240
although you know the number of people

0:24:40.480,0:24:45.840
in this particular group was not as

0:24:42.240,0:24:47.679
large as any other group. Um but uh I do

0:24:45.840,0:24:49.120
think it was very reassuring. It it is

0:24:47.679,0:24:51.200
also important to note that these are

0:24:49.120,0:24:53.440
observational studies and so people are

0:24:51.200,0:24:56.159
not randomized into these into these

0:24:53.440,0:24:58.559
groups. These are self- selected um

0:24:56.159,0:25:00.799
runners and and this goes for any kind

0:24:58.559,0:25:04.320
of analysis that you would do that these

0:25:00.799,0:25:05.840
are all self- selected um

0:25:04.320,0:25:07.440
people who are self- selecting to

0:25:05.840,0:25:09.919
participate in the activities of

0:25:07.440,0:25:11.279
interest. Um and so you have to bear

0:25:09.919,0:25:14.000
that in mind when you're making

0:25:11.279,0:25:15.520
interpretations for um the findings that

0:25:14.000,0:25:19.200
you see.

0:25:15.520,0:25:21.679
So, I just gave you a little taste of

0:25:19.200,0:25:23.520
how to use the data and you can see that

0:25:21.679,0:25:25.120
we've already looked at running. Uh,

0:25:23.520,0:25:26.960
there are other activities that we've

0:25:25.120,0:25:29.200
looked at so far and that includes

0:25:26.960,0:25:32.240
strength training, swimming, bicycling,

0:25:29.200,0:25:36.159
American football, gardening, walking,

0:25:32.240,0:25:38.080
baseball, basketball, and tennis.

0:25:36.159,0:25:40.240
Um, so I mean there's obviously much

0:25:38.080,0:25:43.840
more that can be done. some of the what

0:25:40.240,0:25:45.120
we um have done has been driven by um

0:25:43.840,0:25:47.120
activities that people have more

0:25:45.120,0:25:50.240
participation in and that there's more

0:25:47.120,0:25:52.880
interest in knowing about um but some of

0:25:50.240,0:25:57.120
the activities are um too low in

0:25:52.880,0:25:59.520
frequency to to really study um and then

0:25:57.120,0:26:01.600
the final thing that I wanted to say in

0:25:59.520,0:26:03.600
um our talk this is my final slide here

0:26:01.600,0:26:06.000
is really a recommendation of what not

0:26:03.600,0:26:08.640
to do so um I think there's this

0:26:06.000,0:26:10.960
knee-jerk reaction to want to compare

0:26:08.640,0:26:13.039
people who do one kind kind of activity

0:26:10.960,0:26:15.120
against people who do another kind of

0:26:13.039,0:26:16.799
activity. And because of the way that we

0:26:15.120,0:26:18.240
have administered this questionnaire, I

0:26:16.799,0:26:22.000
really don't think it's a good strategy

0:26:18.240,0:26:24.960
because a person who uh swims could also

0:26:22.000,0:26:26.880
be a person who runs and then you're

0:26:24.960,0:26:29.520
comparing that person against him or

0:26:26.880,0:26:32.400
herself. Um, and that conceptually

0:26:29.520,0:26:34.960
doesn't seem appropriate. So it's a much

0:26:32.400,0:26:36.880
better approach to look at one activity

0:26:34.960,0:26:38.960
at at a time comparing those who

0:26:36.880,0:26:41.520
participate in the activity to those who

0:26:38.960,0:26:44.400
don't or comparing those who participate

0:26:41.520,0:26:46.799
at a higher level in that activity um to

0:26:44.400,0:26:48.880
those who compare who participate either

0:26:46.799,0:26:51.760
at a lower level or do not participate

0:26:48.880,0:26:54.159
in activity.

0:26:51.760,0:26:56.159
So that's the end of my talk and I'm

0:26:54.159,0:26:58.640
happy to take questions but um we're

0:26:56.159,0:27:00.559
taking a little hiatus for our uh

0:26:58.640,0:27:03.360
webinar for the summer and we will come

0:27:00.559,0:27:07.520
back in the fall and this is a QR code

0:27:03.360,0:27:11.640
for our um OAI knowledge base um and

0:27:07.520,0:27:11.640
check there for updates.

0:27:13.039,0:27:17.600
Great. Thank you so much Dr. Lo for

0:27:14.960,0:27:20.000
that great talk. Um, yeah, we have a

0:27:17.600,0:27:21.919
couple minutes for Q&A, so if anyone has

0:27:20.000,0:27:24.559
a question, you can type it into the

0:27:21.919,0:27:27.360
chat. Um, I have a question for you

0:27:24.559,0:27:29.039
here. Um, how do you handle a history of

0:27:27.360,0:27:31.600
injury which could be caused by an

0:27:29.039,0:27:34.320
activity or influence um, what

0:27:31.600,0:27:37.919
activities someone participates in?

0:27:34.320,0:27:41.039
That's a good question. So the question

0:27:37.919,0:27:42.720
about injury is not directly in the

0:27:41.039,0:27:46.320
leisure physical activity questionnaire,

0:27:42.720,0:27:49.520
but we do have questions about injury in

0:27:46.320,0:27:52.880
the main data set. So there's a question

0:27:49.520,0:27:56.320
about like have you injured yourself um

0:27:52.880,0:27:58.159
injured your knee that has led to not

0:27:56.320,0:27:59.360
being able to walk for two or more days?

0:27:58.159,0:28:02.080
I think that's the question. I don't

0:27:59.360,0:28:04.080
know exactly the wording of it, but um

0:28:02.080,0:28:06.000
basically it asks if you've had an

0:28:04.080,0:28:08.080
injury that would sort of qualify as

0:28:06.000,0:28:10.399
that. And then you would there's

0:28:08.080,0:28:13.039
actually one question that asks for age

0:28:10.399,0:28:15.120
25 and younger and then at the time of

0:28:13.039,0:28:17.919
entry into the OAI it also asks a

0:28:15.120,0:28:20.720
similar question. So I can't like

0:28:17.919,0:28:24.480
connect the two directly like hey this

0:28:20.720,0:28:26.240
person had an injury from baseball. Um

0:28:24.480,0:28:31.440
but I can say hey the person had an

0:28:26.240,0:28:34.080
injury at this age range um or not.

0:28:31.440,0:28:36.159
Um another question the survey may be

0:28:34.080,0:28:37.840
long for some people. Did most people

0:28:36.159,0:28:40.159
who started the survey finish their

0:28:37.840,0:28:42.559
survey?

0:28:40.159,0:28:45.039
Um that's a good question. So it was

0:28:42.559,0:28:47.120
take-home questionnaire. So um I don't

0:28:45.039,0:28:51.440
know how many people needed help when

0:28:47.120,0:28:54.480
they um joined sorry when they um came

0:28:51.440,0:28:57.840
to the 96-month visit. um that was just

0:28:54.480,0:29:00.640
our strategy of trying to increase um uh

0:28:57.840,0:29:03.919
participation in the completing the

0:29:00.640,0:29:06.240
survey. Um I don't remember offhand

0:29:03.919,0:29:08.320
exactly how many didn't um complete it

0:29:06.240,0:29:12.320
but there were some people who attended

0:29:08.320,0:29:14.159
the 96-month visit and then um didn't um

0:29:12.320,0:29:17.039
complete the survey but I if you look at

0:29:14.159,0:29:21.679
any of uh our papers we do give like a

0:29:17.039,0:29:24.480
little um a flow diagram of um how many

0:29:21.679,0:29:28.240
did not complete it. I want to say it

0:29:24.480,0:29:32.000
was maybe like 15% something like that.

0:29:28.240,0:29:34.799
Um but don't quote me on it.

0:29:32.000,0:29:36.799
Okay. Uh, thank you for that. I guess

0:29:34.799,0:29:38.960
that's about all the time we have for

0:29:36.799,0:29:40.799
today. So, thank you again, Dr. Lo, for

0:29:38.960,0:29:44.080
your excellent presentation and thank

0:29:40.799,0:29:45.919
you all for joining us today. Um, as Dr.

0:29:44.080,0:29:48.320
Lo mentioned, we will be taking the

0:29:45.919,0:29:50.960
summer off, but we'll resume the webinar

0:29:48.320,0:29:54.480
series again in the fall. So, keep an

0:29:50.960,0:29:57.360
eye out for the update. I sent some

0:29:54.480,0:30:00.000
links in the chat for you to keep up

0:29:57.360,0:30:02.720
with us and also a link to the post

0:30:00.000,0:30:04.640
webinar survey. And so that's all we

0:30:02.720,0:30:06.720
have and thank you again and hope to see

0:30:04.640,0:30:09.960
you in the fall.

0:30:06.720,0:30:09.960
Thank you.