 Longitudinal Study on Aging webinar series. My name is Ina Wobin, I'm the managing director of the CLSA, and I will be facilitating the webinar today, which also means that after the introductions and the presentation by Dr. Orna Daniu, I will be facilitating and presenting your questions to her for answer. So today provides a great opportunity to get to know more about other longitudinal studies on aging, in this case the Irish longitudinal study on aging. And we are pleased to have Dr. Orna Daniu with us today, who's going to talk about mobility in older adults. So Dr. Orna Daniu is a project manager for TILDA, which is a study on aging based in Trinity College in Dublin, and there she is responsible for the overall planning, execution and management of the TILDA data, collection process, and as well to facilitate research and policy objectives. Her current research interests focus on the factors influencing walking and mobility in older Irish adults, with particular focus on how these can predict adverse outcomes such as falls, disabilities, and cognitive decline. And prior to joining TILDA, Orna lectured at the University of Limerick and the University of Edinburgh. So with that I'm going to, I'm going to give the floor to Orna to give us her presentation. So Orna go ahead. Great, thanks Nina, and thank you for the introduction, and hi to everyone who's joined the webinar today. So like Nina said, I work on the Irish longitudinal study on aging. I'm not sure how many people are familiar with the study, so I'm going to start off by just giving you a brief background to the study, and then after that I'll talk about mobility and what kind of research we've done to date using the mobility data that we have in TILDA. So TILDA was set up in 2006, and like most other longitudinal studies, the main driver was to access, to address the challenges and opportunities that population aging presents. And we know that this is partly due to reducing fertility rates, but also due to an increase in life expectancy, which we've observed over the last 200 years or so. As a result of this, there's increasing numbers of people in the older age categories, and the projected increases are thought to be particularly expected in the oldest age groups. So particularly those in the over 85s and the over 100s. And obviously this leads to a change in the ratio of working people to retired people. So it's going to place increased demands on healthcare services and subsequently increase healthcare costs. And so the challenge that we face in Ireland, and like many other developed countries are facing, is to not just focus on extending the lifespan, but to focus on really extending a healthier and happier lifespan. So TILDA is part of this international family of longitudinal studies on aging. When it was set up, it was harmonized really closely with some of the existing studies at the time, mainly the Health and Retirement Survey, so HRS in the US, and ELSA, the English Longitudinal Study on Aging, and SHARE, the Survey for Health, Aging and Retirement in Europe. And since then, a number of other studies have developed, and obviously the big focus in a lot of these studies is to try and ensure that there are harmonized measures collected to allow cross-country comparisons. One of the big features of TILDA, which I'll speak about in a few minutes, is the really comprehensive health assessments that we include. And a lot of the other studies which have come after us have tried to harmonize some of their health measures with TILDA, so obviously again, benefiting that whole cross-country collaboration potential. So TILDA is a nationally representative study. The sampling frame is based on the Irish Geodirectory, which is a listing of all of the residential addresses in Ireland, and it's composed on POST, which is the Irish Postal Service, and the Ordnance Survey Ireland. So our recruitment process is slightly different to many of the other longitudinal studies, and this is because while we have a listing of all the addresses, we don't actually know who lives in all of those addresses. So when our sample was being recruited back in just before 2009, we had to come up with a new process in order to identify our participants. So this process has three stages. The first stage was to divide the country into population sample units or geographical clusters. So when we divide the country up, we had over 3,100 clusters. We then selected 640 of those clusters based on geographical spread and socioeconomic status. The next stage was to randomly select 640 of these clusters, and then the next stage was to randomly sample 40 of those, 40 households within those 640 clusters. So this gave us 25,600 households that were our target households to sample from. At this stage, our social interviewers had to visit all of these 25,000 odd households to check if there were the participants eligible for participation resident in the household. And so our eligibility criteria was that they had to be age 50 plus. However, if they had spouses or partners who were age less than 50, they were also eligible within the study. So after this recruitment process, we managed to recruit over 8,500 participants. You can see the vast majority of these were age over 50. So just under 400 were spouses or partners less than 50. And this represented a response rate of 62% of the eligible households. So we have three methods of data collection. The first is a copy or computer-assisted personal interview. This is conducted by social interviewers who visit the participants in their own home. We then ask the interviewers to leave a self-completion questionnaire with our participants, and they complete this and send it back to us in their own time. And then we also invite all of our participants to complete the health assessment. So this is quite a comprehensive assessment. It takes about three hours. And luckily in Ireland, we're small enough that we can conduct all of our health assessments in just one location. So we ask all of our participants to come to Dublin for this assessment. Obviously, some of our participants are unable to travel or just don't want to travel. So in those cases, we offer a modified home-based assessment to those participants. And all of the health assessments are conducted by trained research nurses. So in our copy and self-completion questionnaire, we cover a broad range of topics. And you can see here the main domains are social, economics, and health. And you can see under each one the typical types of topics or domains that we actually talk about through our participants. So within social, they cover everything to do their household composition, their demographics, transfers to parents and children, their social connections and engagement, activities daily living, any health that they're receiving with that, expectations, transport and housing. Within our economic situation, our economic section, we talk to them about their employment situation, their plans for retirement, their job history, sources of income and any assets that they might have as well. And within the health sections, then we collect information about their physical, cognitive, mental, behavioral health and also their medications. The self-completion questionnaire is where we would collect information about topics which are a little bit more sensitive. So areas that we feel that respondents may not particularly want to speak to and interview about, but that they might be willing to disclose in a more paper and pencil type questionnaire. And moving on to the health assessment, as I said, this is approximately three hours to complete. And it is divided across a number of domains, neuropsychological, cardiovascular, mobility, sensory, anthropometric and other. So we include a really comprehensive cognitive battery. This includes a number of different cognitive tests which cover global cognition, memory, executive function, processing speed and attention. We also include a very comprehensive cardiovascular assessment. So as well as your pathway velocity and standard blood pressure tests, we also include an active stand, which is where participants lie down for 10 minutes and then they stand up. And during these movements, we measure their beat-to-beat blood pressure, heart rate variability, cerebral profusion and respiration. So we get really, really self-cardiobascular measurements during this particular challenge. In terms of mobility, we include standard measures of mobility such as timed up and go and repeat chairstands. And we also include a more detailed assessment of gait using a gait right match, and I'll talk about those in a few moments. In terms of sensory function, there's a big focus on vision, but we also include measures of multi-sensory integration with the use of a sham test. And then finally, we collect a lot of the standard anthropometric measurements, also things like grip strength, heel ultrasound. We take blood samples and hair samples. And then for a sub-sample of our participants, we include accelerometry. So we collect weekly on accelerometry data. We have brain MRIs on a sub-sample, and we also have oral health assessments on another sub-sample. So in terms of where the study sits at the moment in our timeline, our first wave of data collection began at the end of 2009, finishing in early 2011. Every two years since then, we've gone back to do an additional wave of data collection. You can see from the diagram here that the copy and the masturbation questionnaire are included at each wave. So far, we've included the health assessment at waves one and three, and the next health assessment is scheduled for wave six. So at the moment, we have just the beach wave four, and we're preparing for wave five. Okay, so to move on then to the gait and the mobility, and to just talk a little bit about the research that we have done in gait and mobility so far and how we've used this data. So a swift gait is something I'm walking, it's something that a lot of people take for granted, particularly if they don't have any problems at walking. But it's actually a very complex system because it involves inputs from multiple systems within the body, including your mosquito-caulite system, cardiovascular system, nervous system, cognitive system, and so on. Because it involves so many of these different systems, it gives you a really good indication of your overall well-being and the health of the overall system. And more recently, a couple of papers have proposed that it should be included as a fixed vital sign. So in other words, during normal routine assessments, that it would be measured alongside blood pressure, heart rate, and so on. It's not surprising then that there's a number of different factors that would be likely to affect your gait and your walking pattern, both the age and gender, but also things like your mental health. So if you have depression or anxiety, any underlying medical conditions that you might have, cognition, fear of falling, and so on. So if you're talking about a gait impairment, there's a number of different things that this could actually refer to. One of the simplest things that it could be is a reduction in gait speed. So in fact, it could also be on steadiness during your walking pattern, or it could be something that's more qualitative, such as a lack of smoothness or a lack of symmetry. The one that's used most frequently is gait speed, and it's the impact of some factor on gait speed resulting in a slower than normal gait speed. And there's a huge amount of research that has looked at the ability of gait speed to predict various outcomes. And it's been shown to predict things like mobility disability, cognitive decline, falls, institutionalization, and survival. So just to recap again in the measures that we have in Tilda. So we have time different go, which we have collected at all four waves. So initially it was included within our health assessment, but then during our non-health assessment waves, we decided that we would train our social interviewers to also collect and measure the time different go. So that's why it's included at each wave. Our repeated chair stands so far has been included at one wave, and then our gait right assessment has also been included at waves one and wave three. So during our gait right assessment, we ask participants to walk at their usual pace. We also ask them to walk their maximum pace, and then we get them to walk under dual task conditions. So a dual task condition is when you ask the participant to walk while doing something else at the same time. So the two tasks that we include are first of all a manual task, which is to carry a glass of water. That was included at wave one, and then in wave one and wave three, we asked them to complete a cognitive task at the same time. And the task that we chose was to recite every second letter of the alphabet. So you'll see if you look at any of the research on dual tasking that the task of the people choose to ask participants to do varies depending on the study and what the focus actually is. But generally, the more challenging the task that you're actually getting the person to do, the bigger the impact it will have on a person's walking pattern. Typically, what you'll see is that it has an impact on their walking speed, so reducing their walking speed. And in some cases, it makes them walk a little bit more in a more unstable kind of walking pattern. The whole purpose of including a dual task is to assess how people react to having to divide their attention between two different tasks. Okay, so we know that walking speed is related to a huge number of factors, and we've looked at a number of these using the tilde data. And so I'm not going to talk about all of these. And this just gives you an indication of some of the papers that we've published in this area. But I'm just going to focus on fear of falling and also then depression and antidepressant. Fear of falling is extremely common in community-dwelling older adults. Depending on how it's assessed, it can be up to 44% depending again on the sample and how you're actually going to assess it. Not surprising that somebody who has a history of falls might present with fear of falling. But what we've found and what a number of other studies have found is that even people who have never fallen still present with a very high prevalence of fear of falling. And traditionally, there was thought to have two main consequences. The first was increased cautions. So people who had fear of falling would tend to be a little bit more cautious in certain situations, and this could lead them to positive strategies or to decide to do something that was more positive in terms of preventing falls. So for example, it might be being more aware of their limitations and aware of the risks associated with certain activities, such as walking on icy surfaces or during icy weather. The second consequence was avoidance of activity. So this is where people who have fear of falling would avoid all activities, regardless of their ability to actually do them or not. So essentially what was happening is that they would avoid doing everything, even if they were completely capable of doing it. And this would lead to physically conditioning, reduce social interactions, and this eventually would lead to an increased risk. But fear of falling is related to a number of different factors. And you can see some of them listed over here on the right-hand side. And these factors include slow and impaired gait. At the time that we started looking at this, there was very limited research that it was actually looking at fear of falling in gait. Very few papers that we're looking at this using multivariate analysis or using geotask conditions. This is something that we decided to have a look at to examine in a bit more detail. And what we found was that people who had fear of falling had significant gait impairments in comparison to older adults who didn't have fear of falling. And these effects were much more pronounced in those who had activity restriction. The impairments that we saw were reductions in gait speed, reduction in stride length, both of which have been associated with adverse effects in the future. But we also found that people with activity restriction and fear of falling had increased double support and also a wider stance width. And both of these could be suggested to actually increase your stability. So a little bit unclear about what impact that this would actually have on people's risk of future falls. In a follow-up study, we also found that people who had activity restriction, so in combination with poor visual function as assessed with visual acuity tests and contrast sensitivity tests actually had poor mobility and the poor mobility of all the entire group. So again, combination of factors seems to have an even greater effect on your mobility and also then subsequent risk of adverse outcomes. One of the things, what's the limitations of these studies is that they're cross-sectional. So one of the next things that we want to have a look at the associations between fear of falling and activity restriction to see if they are associated with an increased risk of fractures of falls in our longitudinal data and to see if these gait impairments actually mediate these risks. Regardless of that, the results still highlight the importance of fear of falling within this older sample and also highlight the importance of assessing gait in people who have fear of falling. More recently then, a new model has been proposed to describe the impact of fear of falling. And in this model, one of the main differences is that they highlight that this pathway between fear of falling and activity restriction may not be the only pathway leading to falls. So these authors also highlight that the potential for anxiety and being able to realistically assess your own balance abilities can also have an impact on fear of falls to your pathway that leads to your balance confidence and also your actual balance performance. So in Tilda, we ask our participants to rate their steadiness while walking. So we were able to use this measure to have a look at the associations between this and fear of falling and activity restriction independent of the objective measures of gait that we have. So what we found was that people who reported on steadiness during walking were twice as likely to report fear of falling and four times more likely to report activity restriction after two years' follow-up. Then we then adjusted for multiple confounding factors which you can see down here in the bottom. We found that the effects were attenuated but they were still significant for both of these outcomes. We then adjusted for all of the same confounders but additionally for usual gait speed. And what we found was that the relationship with fear of falling was no longer significant. However, again, having on steadiness of baseline, you're still twice as likely to report activity restriction and follow-up in comparison to those who didn't report on steadiness. So this highlights that despite the fact that usual gait speed is such a strong measure and so useful in both a clinical and research setting, these self-reported measures of balance still seem to reflect something which isn't picked up in these objective measures of walking. So again, it highlights the importance of both of these components and how they can both add something in the predictive ability for outcomes such as fear of falling and activity restriction. A couple of implications then from this work. First of all, I suppose it's just highlighting the importance and the high prevalence of on steadiness. So in the Tilda sample, the entire group, 34% of them reported some level of on steadiness. This is a huge proportion. You know that from previous research that people who do report that they have balanced deficits tend to have reduced social, functional and physical activities. And obviously you can imagine how this could have quite a severe impact on their quality of life. Given how easy it is to ask people about balance and ask them to rate their balance. It seems really clear that deficits should be included within all falls assessment tools and also within all of the falls guidelines. And for most of them, it is included. However, there's still some where it doesn't specifically ask you to ask people about their balance. So this seems like something that should be a very clear component of these decisions. Obviously then, by identifying that somebody has a balance impairment, you can ask further questions and do further assessments to identify the potential causes of this balance impairment. And then obviously make the necessary adjustments to try and reduce the impact of this. Okay, so moving on then to depression and falls. Both depression and falls are extremely common in older adults. This model by Ioboni was presented in 2013. And it highlights the really strong relationship between the two. In the middle of the diagram, you can see that there are a number of common risk factors associated with falls and with depression. But the model also highlights that there are a number of depression-related factors and factors relating to antidepressant-based treatment of depression, both of which can lead to a consequence. And then if somebody does fall, you can then increase the risk of your depression as a result of the impact of the falls. So you have this bi-directional relationship where both of them can lead, each of these can lead to the other. Again, we were interested in looking at the impact of depressive symptoms and antidepressants on gait and mobility. Maybe to see whether this potentially could have an impact or a mediating effect on this relationship between depression and antidepressants and falls. Again, there was very little research in this area when we started looking at this. Most of the studies, again, were limited to small samples and that didn't adjust for multiple factors. And again, didn't look at dual task walking. So what we found was that people who had depressive symptoms had slower gait speed and shorter stride length in comparison to people who didn't have depressive symptoms. And you can see that just on the graph over here. When we looked at people who are taking antidepressants, we saw the same effects, except you'll notice very clearly that the size of these effects are almost double in those with antidepressants in comparison to those with depressive symptoms. So those with antidepressants, for example, have at 15 centimeters per second slower than those who are not in antidepressants, which is quite a huge effect for our library analysis. When we then adjusted for multiple contenders, what we saw is that the effects between depressive symptoms and gait and pyramids were no longer significant. So they were pretty much reduced back to zero. However, when we adjusted our models with looking at the antidepressants in gait and pyramids, we found that the effects were reduced, but they were still significant. And they were basically reduced by approximately half. So we're still looking at a reduction in gait speed of almost eight centimeters per second. So again, a very substantial change in gait speed. So there's a number of potential explanations for this. Obviously, when people get older, they tend to have more chronic conditions, so they tend to be on more medications. And the way that medications actually impact people tends to change as people get older. The combination of this and the fact that people are more likely to be taking multiple medications increases the risk that there might be drug-drug interactions or that they may be more susceptible to the side effects of drugs such as antidepressants. Second potential explanation is that people who are an antidepressant, that this just reflects a more chronic or persistent level of depressive symptoms. So in other words, those with worse symptoms were the ones who are more likely to go and seek medical treatment and therefore more likely to be prescribed antidepressants. Currently, then, it's possible that vascular pathology and white matter lesions may play a role. These have been associated with both mobility impairment and also depression. And there was also some recent papers which found that white matter lesions were associated with antidepressants with non-depressive symptoms. So again, something which kind of mirrors what we were seeing in this particular paper. So in terms of the implications then, obviously there's a huge risk of gait impairment if you are on antidepressants. And despite the relationship between depression and falls, there seems to be a lack of awareness of the importance of assessing gait in people with depression. If you look at the guidelines for management of depression, they very rarely mention falls in vicar generals and critical falls. And also they don't actually tend to mention gait impairment at all. The first implication would be to highlight the importance of assessing both gait and fall risk in people with depression and the people who are taking antidepressants. Obviously then, medications can have their place, but it's also worth considering the most appropriate needs of each individual person. So looking at their physical needs as well as their psychiatric needs when deciding what is the most appropriate treatment for these particular individuals. So for example, it's likely to be in multidisciplinary and push which might combine cognitive behavioral therapy, exercise and medications. Okay, so that gives some indication of some of the research that we've done looking at specific risk factors and falls. But now I just want to move on to something which has much more practical significance. So something which people do every day and something which is very relevant to mobility and to everyday quality of life. And that's the ability to cross the road of pedestrian crossings. So in Ireland, pedestrian lights follow a sequence that goes from green to amber to red. The green light appears for five seconds regardless of the width of the road. And the amber light is various depending on the road width, but it usually appears or always appears for a period of time that's based on a walking speed of 1.2 meters per second. And this is comparable to lots of other settings in other countries. And so because we had walking speed data, obviously we want to look at how many people walked below 1.2 meters per second and therefore would have difficulty walking across the road. And what we found is that one in three adults over the age of 65 have insufficient time to cross the road based on their walking speed. So you can see in the graph that people in their 50s had about 10% of them were affected. But this increased steadily with age and it affects over 60% of people in the over 80s category. So a huge proportion of people being affected and not being able to cross the road and not having enough time to do this. We know that a lot of people typically these days don't walk and just end in nothing else at the same time. So most people are doing something else whether it's looking at their phones or drinking something, carrying bags or having conversations with other people. And I mentioned earlier about the impact of geotasking and how you're trying to divide your attention between these two different tasks. So this also plays a role when you're doing something which is time dependent and time restricted such as crossing the road. So again, we looked at how many people walked at 1.2 meters per second while geotasking. And what we found was again that this, you have an increased proportion of people being affected with increasing age. Looking at those over the age of 65, three and four people were affected. But this ranges from 50% of people in their 50s up to about 90% of people once you get into the over 80 category. So again, a huge proportion of people being affected by this. After we did this work, we worked really closely with Dublin City Council and as a result of it, then they changed some of the light settings in Dublin to increase the times that were provided to older adults and they also modified some of their mechanisms that they use when they're introducing new lights across the city as well. So that's an example, one example of how you need a specific level of walking speed to do a particular everyday task. But it's also really useful to know what is the good gate speed performance. So at what level are you increasing your risk of certain outcomes? And this paper which was presented by the authority in 2012 does that. So it shows all of the different cutoff points which are associated with various different outcomes. And one of the cutoff points that's used most frequently is one meter per second. And basically people who walk below this level are associated, have an increased risk of a number of different outcomes that you can see here on the graph or on the table, including cognitive decline, hospitalization, death, reduction of accessibility and so on. So that's extremely useful to know what the cutoff points are. But sometimes it's hard to use that if you have people of different ages. When you know that people, for example, people who are much older will have, generally have a slower walking speed than people who are much younger. So what we decided to do until then was to use the gate speed data that we collected to generate normative data for older Irish adults. And so here we looked at the factors that affect walking speed. So things like age, gender and height. And we generated these normative data for these participants. And the whole point of this was so that we would make these normative data available for an Irish population so that they could be used in a clinical context so that people could see where they lay in relation to other people of the same age and same height category. And also then to monitor their improvement, for example, if you're applying an intervention or enrolling them in a certain intervention. And that's just gate speed that we did these normative data. So we generated the normative data for. And in this paper, it's also, they're also presented for a timed up and go for a number of different cognitive measures for height, for weight, bone health, the heel of the tongue and so on. So just a final word then on attitudes to aging. Obviously we've mentioned, I mentioned a lot of the factors which are associated with gaiting, with mobility and the factors that influence us. But one of the factors which isn't often mentioned and doesn't talk too much in the research is the negative attitudes to aging. And there were two papers published in the last few years using tilted data which highlights the impact of negative attitudes to aging and how this can actually impact your cognitive function and timed up and go performance. And essentially what the researchers found is that if you have more negative attitudes towards the level of control that you have over aging and the negative consequences associated with aging that you're more likely to have a poor tough performance so a slower tough performance than if you had more positive attitudes to aging. Again, it's just a very simple way that you need to improve how people can function and thinking about the factors that could influence how they actually function. So again, it highlights the importance of how people talk about aging, how they write about aging and essentially how people perceive the whole aging process. So to conclude then, mobility is an important contributor to physical mental and cognitive health. And it's affected by multiple different factors and we know that it has an impact on future events. It's a very strong predictor of those events. And it would also have an impact on everyday activities. For example, we saw how can it have an impact on people's ability to cross the road. But the good news is that it's modifiable because it's affected by so many of these risk factors. Many of these, there's ways that you can identify the way that you can treat these risk factors that you can reduce the risk or the impact of them on gay speech. I suppose some of the things that we're particularly excited about and tell us about looking at in terms of the rest of the data, obviously looking at the longitudes of relationships between a lot of the information we have in gay speech, but also, for example, looking at the brain MRIs, which will give us a very strong data to have a look at the relationships with cognition, with cardiovascular function, and with falls as well. So just to thank our main funders, also our additional funders who came on board to register the project. And just to highlight that our TILDA data is available. We archive an anonymized data set at ISDES. We also archive the data at ICPSR at the University of Michigan. And on Gateway to Global Aging, again, you can get codes which will generate some harmonized variables so that you can compare TILDA data to a lot of the data from the other longitudes and studies as well. So thank you very much. And I'm happy to take any questions. Thank you very much, Anna, for this excellent presentation. It was very interesting to see you presenting to data. And I was particularly impressed where you showed the practical implications from your findings and how you went to the city council to change the speed of when the light changes. So that's great. For all the attendees, if you have any questions, please type them in the chat box and then we can present them to the speaker. Maybe I can start off with a general question as well to you, Orna. You demonstrate very clearly how mobility is modifiable. Are there any plans to do some intervention studies, particularly when you talked about how there is impact about negative attitudes and aging and how that impacts performance and as well perhaps around the fear of falling? Maybe you can talk a little bit about that. Sure. We don't have any immediate plans to do any interventions in the TILDA sample at the moment. So obviously it's an observational study. So this was one of the challenges as well you might want to intervene and try and help people. And once you identify certain factors that cause an increased risk, you then go in and modify them that changes the overall direction of the study. So it was at limits in some ways what you're actually getting from the data. And having said that, obviously, I think using the data and using the information that comes from this data in other groups to apply intervention would be open to but probably not within our actual core TILDA sample. Yeah, okay. I understand the challenges of doing interventions in a cross-sectional study. But it's very interesting to see those associations that you presented. When you described the studies that you did on walking speed and working with the city council, are you planning to do that across Ireland? Or was this just done in Dublin? Or how did you go about to kind of implement some of the findings from TILDA and work with other partners to actually implement it for public well-being? Yeah, well, we contacted the first thing was to contact the local city council and to find out how their settings are managed across the city. In Ireland, I'm not sure what it's like in Canada, but in Ireland, it varies a little bit depending on the location. So some of the cities are computerized, so the settings you can go in and you can look at what settings are being used across all of the gesturing lights in the city. Whereas in some other locations, if you wanted to change something, it would have to be done manually. So our first border call really was Dublin City Council because they have a computerized system. So I guess it was the easiest place to kind of target a change first. We would be interested in trying to have, like have rolled it out a little bit more across the country. It's not something we've taken forward as of yet, but it's definitely something that I think would be worthwhile, so it's not, obviously not limited just to one particular location. I think there are challenges, though, once you start targeting an area that does rely on manual changes and having to go to each individual light setting to actually do that. Okay, thank you. I had another question about, so the other practical outcomes from your research was that you created the normative data for walking speeds. So have you compared this value to other longitudinal study and how that's different in Ireland than, for example, in other populations? Yeah, we did have a look at that. In general, when you look at gate speed, our sample tends to be quite high-functioning. And so, suppose there are community dwellings, the gate speed assessment was included during the health center-based assessment only. So for example, the people who opted for a home-based assessment who would typically tend to be that bit older, that bit greater, weren't able to complete the gate-right assessment. So that's probably one of the reasons why they tend to be a little bit more high-functioning. And especially when you compare this to other studies, they tend to look like they walk a little bit faster than some of the other studies. Also, some studies don't use, some of them do, obviously, but not all of them use the gate-right. So you can have methodological differences on how gate speed is actually measured. So whether it's measured from a standing start or a rolling start. And so there's another couple of specific factors like that, but in general, they tend to be kind of on the upper end of functioning. Okay, great, thank you. So, Arna, there's a question from our participant, and I wanted to ask how gate speed was measured. I think you touched on it very briefly in your presentation, but maybe you can summarize how the gate speed was measured in your study. Yes, so we have a gate-right match. So all of our participants who attend the health centre are asked to walk along the match twice. And then we, so gate-right obviously gives you an awful lot of measures that describe a person's walking pattern. But obviously one of those is gate speed, and we calculate the average then based on their two trials. Then we also get to do a maximum walk and then our dual task walks as well. Right, there was another interesting question from a participant. It says that negative thinking is associated with poor performance is one of the conclusions from your data analysis. But I guess apparently negative thinking is often a consequence of poor performance. So do you think this is a bi-directional relationship and what do you think which one comes first? Yeah, that's a really good point. I think it is. I think absolutely if you're performing poorly, I think it can maybe compound a belief that you have or compound how you actually feel about your performance. And so the paper that it's got, I wasn't actually one of your authors on the paper, but I think it's an interesting finding. I don't think they, I'm not sure whether they have plans to look at it and to see whether there is a bi-directional relationship with that. I think it would be a really interesting thing to look at though. And I'd imagine that you probably will get this really complex relationship where poor performance can lead to it or your negative perceptions can actually lead to that as well. Okay, thank you. Orna, we have a few more minutes. So if any of the participants want to enter a question, please feel free to do so. And in the meantime, I'll ask you a quick question about your data access. You said that your data from the TILA is available at various locations. Can you talk a little bit more about how people go about to get data access and what the purpose is of having your data sitting in various locations? Exactly, yeah. No, so we archive our data because we want to promote the use of the data essentially. Obviously, we have a huge amount of data. Not all of it is archived. And so we have, we archive the variables that we are ready so that are cleaned, that are ready for analysis. And also that don't pose any risk to the identification of participants. They're available for research purposes. So anybody who's interested in accessing data for research can contact ISDA or ICPSR and just request the data from there. Essentially, it's a very simple process with ISDA, which is the one based in Dublin. If you contact them and give them a brief description of what your research question is and how you intend to use the data once you sign all of the agreements they send you the data then. ICPSR, I think have a similar process, but I know they have a little bit of a backlog. So, probably, one and wave two data is archived in ISDA. Only wave one data is so far, it has been archived in ICPSR, but we're just waiting for them to upload the second data set. Wave three also is actually going to be archived in ISDA shortly, so within the next couple of months. So I'm sure that would be a big benefit for you to access the three waves of data. Okay, that sounds great. I guess one last question's coming from me. As you mentioned in your study design, is that your trained research nurses go to those participants that cannot come to Dublin. Yeah. What is the percentage of participants that you, I guess, accommodate and when you do measurements at home, how does that impact the data that you collect? So, what we've seen is that as the participants get older, a greater proportion of them will opt for a home-based assessment. So in wave three, this is our most recent health wave, 80% of participants who completed the copy also completed a health assessment. And about 85% of those, or 80% of those opted for a home-based assessment. So you're talking about four or six, sorry, 80% of them opted for a center-based assessment. So about one in five opted for a home-based assessment. The home assessments are shorter. So mainly just because there's certain pieces of equipment that we can't bring to the participants' homes, but they still collect pretty much all of the cognitive measures, some mobility measures, so not the gage right, and some of the, all of the anthropometric grip trends, those types of measures as well. I think that was the last part of your question. I'm forgetting what it was. Maybe you could just remind me of the last one. Yeah, I had to unmute myself. I guess if you don't collect as much data in home visits and how that might impact your measurements for gage analysis, for example, but I think you answered that already. Yeah, so it does have an impact because we know that those people, if they did come to the center, are likely to perform slightly more poorly in terms of a lot of the measures. And when we have compared them across the measures that are collected in the health center and the home, they tend to have poor, say, for example, performance on tug, poor performance in the cognitive measures as well. And so in our analysis, what we do is we have, like, weights that we include within the data set, which can help adjust for the fact that some of those are just slightly lower level in some of those tests. Okay, great. Thank you. Arna, there's another question from a participant asking how you actually did evaluate the fear of falling that you reported on earlier. Yeah, so in our copy, we just simply asked people, are you afraid of falling? And then we asked them, like, to what extent? So is it very much you're somewhat afraid of falling? In subsequent waves then, so the data I presented earlier is looking mostly at wave one data with the outcomes then of whether you developed fear of falling at wave two. But we also included the fall efficacy scale international in our self-completion questionnaire in wave two and three. So that is something which is, I'll see there, different constructs. So what would be really interesting in the next, or the next one is to have a look at the role of false efficacy in the relationship between fear of falling and outcomes such as false. So that will hopefully add a little bit more to the whole picture of what's actually going on in relation to fear of falling. Okay, excellent. It's about five minutes to 12. So I think we're gonna close this webinar and I want to thank Dr. Orna Adoniu for an excellent presentation. It's really great to see results being presented from other long-term studies and how it also has implications to the public well-being. So thank you so much, Orna, that was fabulous. We want to let the participants know that the recording of this webinar will be available on the CLA website next week. So you can go back and look at the slides and listen to the presentation again. We also want to let you know that our next CLA webinar is on April 27th where George Hackman will present his research on heart failure and it's about, this is the perfect storm in an aging society. So this will also be a very great webinar. So we hope you will join there. And with this, thank you again, Orna. And we hope everybody has a great day. Thank you so much for attending. Thank you.