 Well, good afternoon everybody. Welcome to the See Let's Say webinar series for today. The way our webinar will work today is that I will introduce our speaker for today, who will then continue with her presentation for about 40 minutes. After that point in time, you can type questions or comments in the chat box, and then I will moderate those questions and present them to the speaker to provide answers to you. You are welcome to enter questions as the presentation is ongoing, but we will hold those questions to post them to the speaker till the end of the seminar. You can see right now there are some instructions for the first time WebEx user, and hopefully you had some time to review those before we got started. And it is my pleasure to introduce to you today our speaker, Katarina Maximova. Katarina received training in chronic diseases epidemiology at McGill University and has been involved in a primary prevention of chronic disease through research on improving key modifiable behaviors, such as physical activity, healthy eating, smoking and obesity. She has expertise in using longitudinal approaches to understand the development of behavioral and biological risk factors during childhood and adolescence from chronic disease outcomes. Katarina holds a new investigator award in prevention research from the Canadian Cancer Society Research Institute for her research program that aims to support the implementation of effective programs and policies to promote healthy behavioral changes amongst Canadians. Since 2010, she has collaborated with the noncommunicable disease division of the World Health Organization regional office for Europe to consult on country capacity for chronic disease prevention. So through this seminar, Katarina will provide us some highlights how we can prevent chronic diseases through lifestyle modifications, which will also be important for the CLSA. As you know, the CLSA holds longitudinal data relevant to the aging population that can be used in different manners as well. So, and I think I forgot to introduce myself. My name is Ina Wolben. I'm the managing director of the CLSA. And with that, I'm going to give the floor to Katarina to continue this webinar and give us her summary of her research. Katarina, over to you. Okay. Thank you very much, Ina, for the introductions. I hope you can hear me well. We haven't pre-tested the mic. I appreciate the opportunity to give a talk about chronic disease prevention and lifestyle modification, highlighting some of the research that I was involved in for the last 10 years and my colleagues' research and just generally talk about issues that get me up in the morning and that I'm passionate about. So let's get started. In the last century, historically, the leading causes of death were infectious diseases, tuberculosis, pneumonia, diarrhea, enteritis. And in the 19th century and even more so in the last decade, chronic diseases or on the international scene, we call them non-communicable diseases or MCD. You will hear me use the abbreviation during the talk. Have emerged as the leading causes of death, disease, and disability in Canada and globally. In Canada, chronic diseases or MCDs account for 86% of all deaths. They put an increasing strain on health systems, economic development, and the well-being of large parts of the population. And in particular, people over 50 years of ages will talk in a few seconds. Cardiovascular diseases, cancer, chronic respiratory disease, and diabetes are considered the four key or four main MCDs. These MCDs are said to have high impact on health and human development and account for the vast majority of the chronic disease burden. At the global level, the United Nations political declarations in 2011 and in 2014 on non-communicable diseases really put MCDs into the spotlight on the global level as a growing and substantive threat to sustainable human and economic development. The image that you see on this slide is the global action plan for the prevention and control of non-communicable diseases that the World Health Organization developed in response to these UN political declarations. And the plan emphasizes the implementation of high-impact, cost-effective, and feasible interventions for the prevention and control of MCDs. Referred to, you may have heard, as best buys or good buys if they don't need all of the requirements to be the best buys. So if we look at the number of deaths in Canada, we see that cancers in 2008 actually became the leading overall cause of death and Canada counting for about 30% of deaths followed by circulatory diseases which account for about 29 or roughly another third of respiratory diseases. However, just looking at the number of deaths can well it can be informative if the number of deaths just gives equal weight to a death at age 90 versus age 25 or age 5 and does not emphasize deaths that occur prematurely. If we look at a measure called potential years of life lost, which assigns additional weight to deaths that occur at younger ages, we see a slightly different pattern. We see that cancers now account for more than double of premature deaths, so roughly 1,500 years per 100,000 population versus 755 years for circulatory diseases if we average men and women. We also see large gaps, huge gender disparities in fact for circulatory diseases and not so much for cancers. To monitor countries progress on towards chronic disease prevention, the global action plan set out a number of targets or nine global MCD targets that countries are aiming to achieve by 2025. What's interesting here is that target number one is a 25% relative reduction in risk of premature mortality from the four main MCDs that I mentioned to you Cardiovascular Diseases Cancer, Diabetes and Chronic Respiratory Diseases. It's referred to as 25x25 target and has been adopted or committed by WHO Member States globally as the overarching target. The other targets that refer to reductions in behaviors such as alcohol, physical activity, salt, including steak, tobacco, halting the rights and diabetes and obesity, they are considered voluntary targets unlike this first overarching target. So we know based on our current understanding that about 70% of chronic diseases are preventable through behavior modifications. Of course this figure varies depending on which disease we're looking at, whether it's cardiovascular cancer and which particular cancers we're looking at. But generally it's believed that adopting the behaviors of a healthy lifestyle can prevent an estimated 70% of chronic disease cases. Of the key behavioral risk factors and these are the ones that are targeted in the global action plan are tobacco control, harmful use of alcohol, unhealthy diet, physical activity and obesity. I don't think today I have time to talk about all of them, but I try to incorporate results on or touch on all of these, perhaps except for alcohol. I will talk most about obesity because this is where I've done most of my work. Obesity is considered the major concern in the quest for to prevent chronic diseases including cancer. If we look at the recent 10 cancer prevention recommendations, we see a lot of behavioral type recommendations. So most of these factors that I identified today for cancer prevention relate to lifestyle. This is an infographic, but let's look at them more closely. These are top seven. The first one relates to obesity prevention. Staying as lean as possible without becoming underweight is number one recommendation and it's considered one of the most important ways to protect against cancer and a number of other common chronic diseases. Other ones relate to being physically active for at least 30 minutes a day, avoiding sugary drinks, limiting consumption of energy than food, eating more variety of vegetable foods, whole grains and legumes such as beans, limiting consumption of red meats and avoiding processed meats, limiting alcohol consumption and limiting consumption of salt food and food processed with salt. So if we turn to obesity as being the number one key recommendation, even though the link between obesity and cancer has emerged fairly recently, the evidence on the link between obesity and cardiovascular disease diabetes is more established. This is a fairly recent risk factor identified for cancer and spilder. There's a lot of ongoing research. We know that overweight and obesity are now generally much more common than they were in the 80s and 90s. Obesity more than doubled since 1990 and it continues to increase. The graph shows increases since 2000 and even though we see that the rates are stabilizing across the country, they're still continuing to climb. In terms of other risk factors, perhaps the greatest success in chronic disease prevention in Canada has occurred with regard to reducing the use of tobacco. Smoking among Canadians has declined by more than one half over the last one third of a century. For example, in 1965, 50% of Canadians aged 15 years and over were smokers compared with 21% in 2002. And this reduction brought important reductions in lung cancer. But nonetheless, the prevalence is at 20% and so there is still some work to be done in their particular segments of the population that they are a higher burden of smoking use. With regard to physical activity and healthy eating, the evidence is pretty sad. We see that 15% of Canadian adults meet physical activity recommendation of being physically active for 30 minutes per day or for at least five days a week, so roughly 150 minutes per week on a weekly basis. And physical activity levels decline with age and are lowest among adults 60 years and over. With regard to healthy eating, more than 60% of Canadians consume less than the recommended daily amount of fruit and vegetable. In 2014, this is the graph that I have on the right, about 44% of females in each age group reported that they ate fruit and vegetable five or more times daily. It's interesting that the rate was high for females than males in each age group consistently. Now, let's talk more about obesity. As I said, this is where a lot of the work that I have done in the last 10 years and talk about obesity prevention and weight management as we call it. So with regard to obesity prevention, we talk about prevention of weight gain, primary prevention, and weight loss, promoting weight loss in those who already overweight and obese, so secondary prevention. An important aspect, however, of obesity prevention is preventing weight regain, that's the dotted line. Why am I highlighting this? Because systematic reviews and meta-analysis of obesity prevention and prevention consistently show that modest improvements in physical activity and diet do occur. These improvements in clinical populations are significant and clinically meaningful, but these improvements are often transient and are not sustained in the long term. The key to effectiveness and sustainability of behavioral modification is long-term adherence to the physical activity and healthy eating regimen. Because sustained physical activity and healthy eating levels are needed for continuous benefit, but most individuals relapse from adherence and it remains a challenge. This graph is from a study published in 2007 based on 200 overweight adults that were assigned to either standard behavioral treatment with an exercise goal of 1,000 kilocalories per week or high levels of physical activity with 25 kilocalories as a goal. And we see that it resembles the graph that I just showed you, after while the levels declined initially during the six months of the intervention, they crept up and at 30 months there were no difference from the baseline. So the weight loss maintenance intervention was small in the short term and diminished even further over time. What is it that we need for successful weight loss? The National Weight Control Registry wanted to know precisely that. They followed individuals who succeeded at long-term weight loss maintenance. To be eligible for this study, the individuals might have maintained weight loss of at least 30 pounds for at least one year. They followed 6,000 people and on average these individuals maintained weight loss of at least seven pounds for over for about six years. And among the primary strategies for over 90% of these adults were a high level of physical activity and consistent self-monitoring of weight, diet and physical activity. This meta analysis of 64 obesity prevention interventions in children found that one factor that was consistently associated with larger intervention effect was the recruitment method such that children who self-selected or volunteered to receive an intervention were exhibited better response than children who were assigned to receive an intervention. This suggested to me that adults or children who successfully maintained weight loss over a long term were highly motivated to do so. So I became interested in this topic of motivation and how it affects weight loss. The issue of recognition of overweight on the part of individuals, whether it's an important component of intervention strategies that are targeting behavior and modification such as increased physical activity and weight loss. This hypothesis is premised on theoretical models of behavior change that I have listed here. And while these theories have different theoretical underpinnings on how behavior change occurs, what's important and each of them emphasizes that individuals must perceive that they or recognize that they are at risk in order to change their lifestyle behaviors. Unfortunately, the evidence in the last 10 years that's emerging shows that individuals do not perceive significant proportions of individuals, do not perceive their overweight status. And the discrepancy is more common among overweight and obese children and adults. So this table shows that among overweight adults, 43% of men and 18% of women perceive themselves to be healthy weight or underweight. This is another example showing that only 40% of overweight men reported being overweight, meaning that 60% of them thought they were normal weight. I became interested in this topic and there was a study that came out in the New England Journal of Medicine in 2007 using data from the Framing chemical cohort study. It showed that having a social network with a high prevalence of overweight was associated with weight gain among adults. These researchers assessed the nature and extent of person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. And they found that a person's chances of becoming obese increased by 57%. If he or she had a friend who became obese among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40%. If the spouse became obese, the likelihood that the other spouse would become obese increased by 37%. So then in 2008, we published a study using a representative sample of Quebec children and youth showing that parent body mass index and schoolmate body mass index, so it was the average BMI of children in the school, made a difference for what were important, such that what the heavier the parents or the peers, the more likely youth were to misperceive, to underestimate their weight. The study point that the environment is important, but also motivation in obesity prevention is important. And the clinical practice guidelines that were revised in 2015 now recommend assessing readiness and barriers to change prior to implementing healthy lifestyle plan for weight control or management. Now, while previous studies hypothesized that acknowledging excess weight may motivate action and change, very few studies actually investigated if weight status misperception is related to lifestyle behaviors that people engage in such as physical activity or healthy eating or other lifestyle behaviors. This study was using data from M. Haynes in the United States based on a stratified multi-stage probability sample, large 10,000 people, and they asked if people consider themselves to be overweight, underweight, or about the right weight using measured BMI to juxtapose these perceptions. And they found that weight misperception was associated with less interest in or attempt at weight loss and less physical activity. The results for energy for dietary intake were less consistent. So where did we take this research next? I worked with the pediatric weight management clinic in Edmonton to look at the degree of readiness to improve nutrition and physical activity habits. And we looked at medical records of 100 and 13 children who were enrolled in this outpatient weight management clinic. Parents completed the weight loss behavior stage of change scale that you see on the right to assess their degree of engagement in making healthy changes to their lifestyle behaviors such as increasing dietary portion control, food and vegetable intake, physical activity, a planned exercise, and et cetera. And the responses were linked to the transthoretical model of behavior change. Then we looked at these responses and juxtaposed them, linked them with the actual behaviors of these parents. Now these are the parents of children who are already who were referred and were enrolled in weight management. What was surprising to us is that we found two-thirds, before we look at the results in the table, look at the sample size of the N equals 43 and N equals 70 of parents who were more ready versus less ready to make these behavioral type modifications. What we see is two-thirds of parents presenting for obesity management with their children were in lower stages of readiness, pre-contemplation, contemplation, preparation. But the more ready group, we do see some differences to those who are in higher stages of readiness, i.e. action or maintenance. They have more positive lifestyle habits related to portion sizes, dietary fat consumption, food and vegetable consumption, physical activity, and were more likely to meet the daily recommendations for food and veg and physical activity, which is what you see in this table. As a next step, we wanted to continue this research and in motivation. We applied to CHR and received a grant to develop and assess a brief web-based intervention that was designed to motivate parents to prevent childhood obesity through primary care setting. We engaged parents when they were waiting for a pediatrician appointment in a primary care clinic, waiting room. They used iPads to fill out the questionnaires and respond to the intervention. So this was a double-blinded parallel signed run by control trial, and parents were block-randomized into one of the four brief interventions. So what was the intervention? The intervention was parents responded to a question about their child's lifestyle behaviors, for example, daily screen time portion size, and then their answers were contrasted on the iPad against descriptive data or normative data from the Canadian population, for example, the Canadian Health Measure Survey, or injunctive data, for example, using national guidelines such as Canada Food Guide. So for example, if you ask on a typical day, how many minutes of moderate to vigorous physical activity does your child get, and mom selects 16 to 30 minutes per day? And then mom's response could be contrasted against descriptive data. You said your child typically gets this many minutes per day. Did you know that Canadian children of the same age and sex as your child typically gets 47 minutes of MVPA per day? Or her response could be contrasted against injunctive data using Canadian guidelines. Did you know that Canadian guidelines recommend that children should get 60 minutes of MVPA per day? So this is based on Bandura's social cognitive theory in trying to create a cognitive discrepancy, because previous studies established that cognitive dissonance can have a strong influence on intentions to change behaviors. You have to be careful to create an optimal discrepancy, because discrepancy that's too small may not influence intention, but the one that's perceived to be unattainable will also dampen the motivation to change. We just had a PhD student who defended her PhD using this work, and the results are being published. I won't talk about what we found, but I want to refer you to or show you an article that was published in the New Yorker in 2004 that discussed the same phenomenon of weakness perception, but with respect to autism spectrum disorder and the belief that autism is caused by vaccinations, the MMR vaccine in infancy, despite the fact that the literature has refuted this relationship. And the font may be too small, but what did they find? They found a whole lot of nothing. The result was dramatic, a whole lot of nothing. None of the interventions worked. The first leaflet focused on a lack of evidence, connecting vaccines and autism seemed to reduce misperception about the link, but it did nothing to affect intentions to vaccinate. It even decreased intent among people who held the most negative attitudes towards vaccine, a phenomenon known as the backfire effect. So the researchers say that this is quite depressing. Another challenge when we work in weight misperception and trying to correct misperception is the heterogenic consequences such as increased waste per occupation, stigma, body dissatisfaction, poor self-efficacy, self-esteem, and the potential to provoke eating disorders. And we have recently shown in children that accurate weight perceptions are indeed a risk factor for poor psychosocial health. And girls were particularly vulnerable to the influence of having accurate perceptions of access weight on psychosocial health. I don't believe there are any longitudinal studies on this topic yet. I want to change gears here a little bit. And I was told that there's a fair number of students who are participating in this webinar. And I just want to talk about how do we study change. So when we look in this field of chronic disease prevention, we're interested obviously in the change in risk and we're changing, we're interested in change in risk factors. So the exposures are changing, outcomes are changing. How do we study this? How do we make sense of this? How do we detect a signal? How do we arrange the data? So there are obviously many, many different ways and depending on what type of data you have. Here, I show a study by a colleague, Mathieu Belanger, who showed a sustained participation in physical activity over five years. Now, this study was following youth for six years and they were evaluated. Physical activity was assessed every three months. So you're able to construct these trajectories of sustained participation in physical activity and then break it down whether it's light, moderate, or vigorous. Another way to study the same data, you see here 20 waves from the same study, is to use a latent approach or latent growth curve modeling. Growth here does not necessarily mean that it's positive. Growth could be negative. For example, it's trajectories of depressive symptoms across the life course. But here, we are rather than specifying whether it's light, moderate, or vigorous, we are letting the computer generate or the software generate these trajectories for us. And then as a next step, here's another example using trajectories of leisure physical activity in adults from who participated in the Canada Fitness Survey. Again, finding trajectories of people. And then what do we do? How do we make sense of these latent trajectories because we don't really know what they are? Then as a next step, we found these trajectories. We found people, separated people into whether they're active, increased, decreased, or inactive, as for example, in this paper. And then as a next step, we would use regression analysis to try to figure out who are the people that belong in these classes. So in this example, we see that women, older participants, and those with lower household income, lower educational attainment were significantly less likely to follow active versus inactive trajectories of lifestyle, of physical activity. This essentially shows similar trajectories for screen time. And I think in the interest of time, this shows trajectories for smoking, again, using latent approach, and then trying to relate these two social demographic parameters that you cover in the data, trying to figure out who these people are. But this approach, it's modeling exposure, but it doesn't really tell us much about the outcome with we're trying to identify who these people are in these groups, but we don't know what their risk for chronic disease is. How do we study change behavior modification in lifestyle behaviors? I think the literature on smoking cessation is probably the most advanced and has been shown that following smoking cessation, the risk for coronary heart disease, heart attack, stroke, risk of death from lung cancer, risk of coronary heart disease, improves and reaches that of a non-smoker following a certain number of years we have after one year, after five to 15 years, after 10 years, after 15 years, after 20 years. How do we study this? So how did this study do this? You would look at smoking cessation histories using questionnaire data. This is an example of a study that was published in New England Journal of Medicine just a few years ago using data from 200,000 women and men 25 years and older who were interviewed and they obtained the smoking cessation histories. And then these questionnaire data were related to causes of death that occurred by 2006 I believe. And this shows, this figure shows, the effect of smoking cessation or quitting smoking on survival to 80 years of age according to the age at which time you quit smoking. And shows that the horizontal bars here show the number of years that you would gain and we see that between four and 10 years can be gained. Life expectancy could be increased from four to 10 years among smokers who quit depending on their age at the time of smoking cessation. This is another way of looking at the same data and comes from the same study shows the risk of death among participants who continue to smoke versus those who quit smoking according to the age at the time of cessation. So this is using mortality using questionnaire data and linking it with mortality you could do the same thing for incidents such as cancer for example using registry data. Now when we talk about risk factors such as healthy eating and physical activity things become more difficult to study. Why? Because obviously it takes more time and effort to gather the data on dietary exposures and it's also more difficult to detect an effect. This is a study that is just coming out in public health nutrition that we published with the postdoctoral fellow leading this work in children using dietary quality index that characterized diet into dietary adequacy, variety, moderation, and overall balance and then we linked it to two-year perspective changes. So just change between time one and time two so delta simple change between time one and time two. We are just undertaking the work to study change in dietary exposures so modification of diet and I can tell you that it's not not so easy. This study from a randomized controlled trial again looking at not primary prevention but secondary prevention looking at influence of adherence to behavioral recommendations, diet, exercise, and smoking modification on the risk of cardiovascular events or recurrent cardiovascular events, cardiovascular infarction, stroke, cardiovascular death, and all-cosmon mortality that was documented at one, two, and six months so the horizontal bars at the bottom using 18,000 participants from 41 countries. What's interesting before we even look at the effect sizes looking at the number of patients so these are as I said this is secondary prevention so these are people who already had an event a cardiovascular event and looking at adherence we see that about one-third of smoking persisted in smoking so as you move down on the number of patients at the top you see never smoker, diet or exercise so this is our role model and then as we move forward we see that a third persisted in smoking similar about 30% did not adhere to diet or exercise recommendations about and only 30% reported adherence to both diet and exercise. What looking at the effect sizes we see that patients who reported persistent smoking and not adherence to diet and exercise had a 3.8 fold increased risk of recurrent cardiovascular events MI, stroke or death compared to never smokers who modified their diet and who followed an exercise regimen but again this type of study would not tell us about primary prevention it's clinicians that I work with often they will love to say to a patient if you were to change your increase your fruit and vegetable intake your risk of cancer would increase by would decrease rather by this much and what how can we study changes in diet and changes in risk I've recently reviewed literature on diet physical activity and cancer prevention and this is this comes this graph comes from the American Institute for Cancer Research and World Cancer Research Fund they have what's called a continuous update project CUP that continuously monitors the evidence on diet physical activity and cancer risk and it's interesting looking at this evidence for the most part these were this evidence comes from case control studies as we know case control studies are prone to recall bias selection bias but the evidence from the methodologically stronger cohort studies is still very limited and less consistent for example with regard to red meat and processed meat consumption the WHO International Agency for Research and Cancer IRC recently published a monograph that they evaluated over 800 studies worldwide on meat consumption and cancer risk and interestingly there were about 14 cohort studies on this on the topic of red meat consumption and 18 cohort studies on the topic of processed meat consumption how many studies were in Canada one and this was not a primary prevention cohort this was a secondary prevention cohort of breast cancer screening based on the basic cancer screening cohort for the fruit and vegetable intake for example the initial evidence that was coming from case control studies showed substantial risk odds ratio of two for diet related cancers but as evidence from cohort studies started started to come out it was much more much lower showed much lower evidence of risk and this continuous update project downgraded the evidence in relation to fruit and vegetable consumption and cancer risk now it's you see the convincing evidence the red squares only for the upper GI cancers I'll just talk for a few minutes I'm seeing that I'm a little bit over time but another way of approaching change studying this the the the topic of change in diet is looking at diet of people who have immigrated from one country to another and substantial body of evidence on the link between diet and cancer it comes from what's called migrant studies and it shows that the adoption of western type diet and lifestyle in countries with high incidence high prevalence of unhealthy behaviors can substantially increase cancer risk for colorectal and hormone related cancer such as breast and prostate cancer on the other hand it can also lower risk for in Asian population for stomach cancer because in Asia diets are high in salt and nitrite containing foods again looking at differences between migrant populations and risk of mortality cardiovascular mortality cancer mortality shows that their health advantage help immigrants are healthier on arrival to the host country but this health advantage wanes with time both 10 years of residence in the host country and it's been demonstrated for all cause cancer and cardiovascular mortality there is evidence John Kernar recently published a paper linking as immigrant status with cancer cancer incidence and self-reported chronic conditions but again this this type of evidence leads us to speculate that it's related to lifestyle behaviors but there's very little evidence to date to show that to show this difference we have published a couple of papers one on obesity showing in children showing differences between Canadian born first generation and second generation immigrants and saying that that demonstrating that if you look at the rate of increase in body mass index between first generation second generation and native born that this health advantage that immigrants possess really wanes within the first generation it's really lost within the first generation we've also demonstrated this for smoking I have not been involved in studies that pertain to physical activity and maybe the last slide and I will I'll stop I think a lot of the studies on obesity in a environment that I was showing you earlier on immigrant on immigrants adopting the western style lifestyle behaviors really points us to the importance of environment and I think when we talk about lifestyle behavioral modifications it's important to recognize and evidence is really beginning to accumulate to a characterize these environmental these characteristics of the immediate environment in which people live and work and demonstrate the link with overweight and obesity and chronic disease risk I will stop there thank you very much thank you very much Catrina that was very interesting to see your work on how you've looked at preventing chronic disease from various large data sets and I see a nice compliment to you already from Mary Noel to all participants in the chat box so I would like to invite all participants to if you have any questions or comments to type your questions in the chat box and while I'm doing that maybe I can ask Catrina a question from your presentation that kind of pique my interest is where you describe the differences and weight perception between males and females and why you know I'm sure there's an explanation for it but how do you explain that difference and how does it impact you know health and well-being in the long term for men and women in general thank you for this question yes we do see consistently in both children and in adults that women are better at recognizing their overweight again I think here interesting link well it's been shown that women are hypothesized and shown that women are more susceptible to the societal pressures to be thin and has been shown in the mental health literature that they are more affected by this pressure to be thin and their their psychosocial health suffers as a result so and then I guess if women are more you know on top of their well-being or weight in this case it might have a positive impact on their long-term health in your opinion because they're more aware and maybe more keen to make changes to their lifestyles absolutely so this is a double-edged sword right so on the one hand you want people to be aware because it motivates them to engage in healthy lifestyle behaviors but on the other hand we're raising the awareness and the importance of psychosocial or the effect on psychosocial health and that these people may need help to maintain high self-esteem and self-efficacy to engage in these behaviors yeah okay interesting I have a question here from the floor from Dr. Lauren Griffith she asks so you present data as specific risk factors and outcomes but of course and I think you might have alluded to that that risk factors cluster together like obesity and sedentary behavior can you also examine these clusters of risk factors and outcomes together this is a fantastic question thank you so much for asking in fact this is what I was just writing about the literature is emerging as we are understanding that behaviors cluster or what some people in the literature have called core curve so someone who consumes an unhealthy diet is also more likely to not be physically active have an etc and I am not aware of studies in adults but there are three studies in children one by Scott Leatherdale Jill Paradis published a couple of papers on what's called core currents of chronic disease risk factors and relating these in a similar fashion I think the literature that I recently reviewed on dietary factors and cancer risk the one that from the continuous update project it was very interesting to me that this literature looks at isolating the effect of fruit and vegetable intake isolating the effect of meat intake but we forget that these behaviors cluster and core occur okay great I have another indirect question that came to me I know that you mentioned when you were doing an overview of all the studies that there was very few studies in Canada but of course now with Canadian longitudinal study and aging we have large volumes of data and lifestyle behaviors in a longitudinal way what do you think you know how could the CLA data be used for future studies and I know this is not specifically your your area of research but you know what do you think would be kind of the the key gap in knowledge out there that the CLA data could be used for if you can extrapolate from your expertise and the younger population I think that I try to stress through the talk and highlight the key points and I'll certainly make the slides available after the the presentation I think CLA serves as a great resource as you are accumulating data I know that you have baseline information on a lot of lifestyle behaviors already collected I think you can start there I think you are currently maybe collecting a second wave of lifestyle behaviors but also I understand that a huge number of participants close to 90 percent have consented to having the data linked to cancer registries or mortality databases and I think looking at healthy healthy trajectory sorry trajectories of healthy lifestyle behaviors as people age it would be fantastic contribution to the literature how do people end up you know for example with physical activity we know that physical activity declines with age looking at people who have not looking at trajectories of the people of their behaviors looking at their other their other behaviors how can we prevent this from happening okay well great thank you very much Catherine that was a nice plug for the CLSA and I hope that many of you are attending our webinar and are maybe students maybe to go check out our website and see how you can apply to use CLSA data since we're almost close to one o'clock I want to thank you very much Catherine for an excellent presentation that was a really a nice layout of what you can do with large databases and looking at how you can prevent chronic diseases which we all know is is a is a challenge for the aging population so thank you very much I also want to attend make the attendee severe that we have two more webinars coming up there is one November 22nd by Dr. Raina Manning of the University of Manitoba and she's actually going to present oh here we are she's going to present data from the CLSA and looking at definitions of social isolation and the second webinar that will be in December will also be looking at data from the CLSA and this presentation will be about pet ownership and social participation and life satisfaction in all our adults in Canada so I hope if this is of interest to our attendees so you will attend for these webinars as well again as we have indicated the webinar will be on our website in a couple of weeks and you will be notified if you have registered for this webinar to go and you can look at the slides or listen back to the presentation so with this thank you very much for participating today and I will hope to see you in the future thank you very much