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Public health intervention

From Wikipedia, the free encyclopedia

A public health intervention is any effort or policy that attempts to improve mental and physical health on a population level. Public health interventions may be run by a variety of organizations, including governmental health departments and non-governmental organizations (NGOs). Common types of interventions include screening programs,[1] vaccination,[2] food and water supplementation, and health promotion. Common issues that are the subject of public health interventions include obesity,[3] drug, tobacco, and alcohol use,[4] and the spread of infectious disease, e.g. HIV.[5]

A policy may meet the criteria of a public health intervention if it prevents disease on both the individual and community level and has a positive impact on public health.[6]

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  • ✪ Understanding Health-Related Behavior
  • ✪ This is Public Health: Infant & Child Development
  • ✪ Community-wide Interventions for Increasing Physical Activity: What's the Evidence?
  • ✪ Public Health Approaches to Whole Health for Individuals with Behavioral Health Conditions
  • ✪ Autism & the importance of adapted physical activity

Transcription

Welcome to the Understanding Health Related Behavior segment of the NIH M Health online course I'm Doctor Donna Spruijt-Metz and I'm the Director for Mobile and Connected Health Program at the Center for Economic and Social Research at the University of Southern California and I'm going to talk to you about health-related behavior today. So what do we know about health-related behavior. I'm going to give you a few examples of what we now we know. We know, for instance, when it comes to diabetes, heart disease, obesity, poor grades. We know for instance that too much sugar in your diet, high fructose corn syrup, saturated fat, breakfast skipping, calories too much calories. We know that all of that increases your risk for obesity, cardiovascular disease for nonalcoholic fatty liver disease. We see children 11 years old and coming to our clinic that have high sugar diet, and they have nonalcoholic fatty liver disease already. What we also know, everybody knows this, that a high-fiber diet, lots of fruits and vegetables. That's going to lower your risk for obesity, cardiovascular disease, and for many other metabolic diseases. What do we do? Well, here's what we do. In 1980 Americans consumed about 120 pounds of sugar per capita per year. In 2010 it's already up to 132 pounds of sugar per capita per year. That's a lot of sugar. And how about gallons of soda consumption, 45 gallons per capita a year and that was back in about 2000 and it's just gone up incrementally and you know, we know that drinking soda is bad for us. We know this. What about physical activity, another great example, we know that high rates of physical activity. moderate and vigorous physical activity for children, 60 minutes a day, for adults about 30 minutes a day, reduces adiposity, reduces risk of breast cancer, colon and other cancers, it lowers your cholesterol and it's also a good treatment for stress and depression. We know that. We also know that if you are physically active you'll be more insulin sensitive and you will be more likely to have better academic performance. There's good evidence for this. It's stuff we know. We also know that sedentary behavior, in other words, time spent sitting watching TV, not moving around. We know that's bad for you, in fact that sedentary behavior is related to obesity, cardiovascular disease, metabolic syndrome, type II diabetes. Kids who hang around more, more inactive tend to also participate in other risky behaviors. In fact, lots of times sedentary behavior is related to premature mortality. We know this. And what do we do? Well, here's a slide from a nationally representative population across the United States and this is physical activity measured by accelerometry, which see on the Y axis is that minutes per day of physical activity and we see on the x-axis is age so children 6 to 11 years old are getting about in general over 80 minutes per day of physical activity. That's great. They should be getting 60 so they're doing fine. But what you see here in America is when kids get to about 11 years of age they basically sit down and they never get up again. So I think we can safely say with these examples, smoking is another example. Our behavior is killing us and here's the part that we can change. We can change a lot, but the thing that is easiest and most important to change to help our health in America is behavior. So, how do we change behavior? It turns out not to be so easy. I want to share some guiding principles of behavior theory with you. Behavioral theory is based on understanding what influences behavior. So you see on this slide, genes and metabolic health. Genes, of course, your genetic makeup is going to influence your health and it's going to influence your behavior to some extent, so the bio ecological model as you see in the third row. So what you see in the slide, actually, is the influence of behavior and examples of how this might influence behavior. For instance, genes and your metabolic health influence your behavior through insulin resistance and allergies and then a model that accounts for that is known in the literature. So, for instance, as I said, genes, if you are more prone, genetically prone to allergies, that might change your behavior, right, you might avoid certain places. Your metabolic health, for instance, insulin resistance, we shown in our lab that insulin resistance at a young age, the more insulin resistant you are the quicker physical activity declines as you go through puberty. The bio ecological model accounts for some of these differences. Emotion is another thing that is a strong influence on behavior, things like fear, desire, need, motivation, impulse. Self-determination theory and dual processing theory talk about emotions as influencers on behavior. Cognition, so what you think certainly influences your behavior, what you believe, what you know. Social cognitive theory is one of the main theories that talks about how cognition influences behavior and there's also behavioral economics and choice theory. So your social environment, your friends, your peers, your family, they definitely influence your behavior, social networks. Theory of reasoned action and system science theories are theories that take your family and your friends into account. The institutional environment, work and school influences your behavior. Social environment, such as economic, socio- economic status, the economy of the country you live in, political policies. These all influence how you might behave. Natural and built environment-- parks, fast food, so if I lived closer to a park, it turns out I might be more likely to use it to be physically active. And of course one of the big influencers on your behavior is your own behavior, habits. Past behavior is one of the biggest influencers of future behavior. I've done it before, I'm going to do it again. And then of course proximal behaviors like the fact that I ate a good breakfast this morning means that I'm feeling perky and here to give this talk to you. If I hadn't eaten a good breakfast I might be feeling a little bit less energetic. So past behavior has a big influence on future behavior, and this has been covered in many different, over 100 theories in social science. So here's what a social science theory looks like. This is a picture of the theory of reasonable action and the theory of planned behavior that was sort of added onto it and I want to walk you through some key terms. So what you see here are what we call constructs. All of these bubbles are constructs. Constructs are stuff that we have to measure, and we're looking at behavioral theory. We have mediators. So what, for instance, if we think that behavior beliefs is a determinant of behavior and then you see this arrow pointing towards attitudes towards behavior so that affect of behavior beliefs on behavior is mediated through the attitude towards behavior and behavioral intentions. This is what this particular model says and then we have moderators. Moderators can be gender moderators can be age, moderator can be social economic status. It's not a'priori up front that might change the way we behaved and change all the mediators as you go through this model. What's wrong with this theory. What's wrong with all the theories. That's what I'm here to talk to you about. But behavior is complicated. It's not linear like the model I just showed you. Our current understanding of human behavior is based on static snapshots of human behavior, but behavior is not like that. It's ongoing, dynamic feedback loops, it responds. My behavior, your behavior response to ever-changing biological, social, personal and environmental events and states across time and situation. New technologies, wearable and placeable sensors, mobile phones, timestamp backend data they are giving us new opportunities to really understand human behavior in real time. So many of these measures bypass a certain kind of bias, bypass human memory, thought. They measure ubiquitously and they measure well. For instance, the Fitbit that I'm wearing now measures physical activity or steps at least in an ongoing fashion all through the day. And I've got that data and its time stamped and I can access it online when I want it. It goes right through my phone. The measures that we have through these new technologies give you real or near real time data so they tell me all about myself on an ongoing basis. It's pretty interesting. You can understand behavior in time and in context so I can know what I did at a certain time and where I was through some of the sensors just in my phone or sensors that I'm wearing and this data can be shared with me. I can share my data with myself or I can share my data with my friends and this kind of real-time data can also be shared with clinicians, practitioners interventionist, such as myself, and to understand your own behavior and others behavior in real time and to give real-time feedback we can harness these new technologies to re-envision our understanding of behavior. Let me give you a little idea. Let's use an example. So we're going to target eating chocolate and because any time I'm in the room there is chocolate in the room. So what you see here is that greenish yellow ball on your screen is to represent how badly I want chocolate and along the line on the bottom you see time of day, so my desire for chocolate is going to change over time of day. It bounces around, right. One of the things that might drive whether or not I eat chocolate is whether chocolate is near me at the time, and of course my relationship, my desire for chocolate and the proximity of chocolate changes over the day. So here I am at around evening and I really want a piece of chocolate and there is one in the bowl next to me. I'm going to eat chocolate, right.. But there are other influences on behavior. How about stress, so now you see my need for chocolate, my desire, the proximity of chocolate and my stress levels all vary over the course of the day and when we get to evening, this particular evening, I'm not really feeling like chocolate, however, Chocolate is pretty close to me and I am really stressed and I might eat chocolate anyway. And of course, what if my friend Darcy comes over and Darcy also really likes chocolate so over the day my proclivity or my desire for chocolate changes, proximity to chocolate changes. Here, I'm pretty close to chocolate and I really want it but I might have to get up and go across the room to get it so I might not eat the chocolate. However, stress levels are really high so I'm thinking about and I like to eat chocolate when I'm stressed and then my friend Darcy comes over. She also likes to eat chocolate and I'm totally going to eat chocolate. So real-time theories of behavior should probably look more like this. Changing, adding new variables, flipping, depending on everything, but we don't know yet, because we haven't had a chance to create these models and we have that chance now using M health technologies. So how can we bring behavioral health into the 21st century. Wherever we go nowadays we leave these enormous digital footprints. Computers of amazing capacity reconstruct our movements from the tracks that we leave, and I'll show you what I mean by this. We leave these digital clues that form a comprehensive image of who we are, what we do, where we doing it, what we're thinking, who we're talking to. And we track and reveal not only ourselves, but each other, using the ubiquitous technologies, social networking sites, and everything like that, as we go. So look at the Internet. The internet was born in 1982 and subscribers per hundred inhabitants worldwide and you'll see that wer'e pretty much at saturation in the developed world and the developing world is meeting this. Here is Amazon prime membership. How many of you are Amazon prime members. When Kindle started the Amazon prime membership really took off and so we're talking about millions of subscribers now and Kindle and Amazon know a lot about you now. They've got your credit card. They've got your address. They got all the choices that you make so every time you make a choice and buy something they know the books that you want to read. They know if you're remodeling your house. The size of your underwear and they also know where you spend your money and how much you have, pretty much. Facebook launched in 2004. We're talking about millions of users, 11 hundred thousand million. Numbers I can't even read off my tiny screen here. Millions of users and it launched in 2004 and it has become such a part of our lives. Google, now there's one for you. Google has Google search, Google drive, Google chat, Google voice, Google groups, Google Earth, Google maps and Google plus. How many of you are Google users. I don't think even Google knows how much they know about each of us personally. And all this data is up there and should maybe be given back to us. And here's the game changer, mobile phones. We are, in the developed world, at 115% saturation. In other words, many people are beginning to have more than one phone. In Italy it's 2.2 X 2 .2 mobile phones per person and in America it's getting to the point that people sometimes have more than one. And the developing world is catching up with us. So we all carry these little computers with us and we don't just use them to make calls. Here some of the things that smartphones are also used for and how they've changed in the last few years. So the light gray bars, 2009 and the darker bar is 2010 so in a year people have been taking more pictures, sending and receiving more text messages. They play games, they send email, they access the Internet., they play music, they record videos, all of that on their smartphones. These mobile technologies are data hungry, context aware and ubiquitous. Let's talk a little bit about what that means. Here is your mobile phone. It's got sensors on there that can sense your behavior like accelerometers and gyroscopes. It's got GPS sensing, so it can tell where you are. It's got sound recognition, so this has been used. Some great work is coming out of several universities where using sound recognition to understand your moods, to understand the quality of your conversations with other people, so that we can tell if you're having a meaningful conversation, or if you are angry. The phone also integrates with wireless data from wearable and deployable sensors. So for instance I'm wearing a Fitbit, I can be wearing another, and many different kinds of bracelets, for instance, that register physical activity or there's a new bracelets the register's bites. There is a fork that measures bites and all these communicate with your cell phone and the data can be sent out to a cloud to be analyzed on the phone or on in the cloud. You can also have place-able sensors around the house. Some wonderful work is coming out of Northeastern University, looking at studying gait in the elderly, where sensors are placed in the hallway and we can see how quickly or slowly they walk in the hallway over a period of time and almost predict through the slowing of the gait when they are going to be ill. So this phone also has pictures, videos, and soon will have heads-up devices that I won't even have to carry this anymore because it will be right on my glasses. Be able to ask things of the Internet right there. The phone has records of who you talk to and how you use the Internet and your text messages and the wonderful thing about the data on your phone and on all of these devices is that it gives these patterns over time, temporally dense data over time so we can look at how your behavior changes over time and give you real-time feedback. So let's say that I'm in a market, and I am doing my shopping and I have cardiovascular disease and I'm supposed to stay away from high sodium and I'm in the aisle of the market that sells canned soups. I could get a message on my phone that would pretty much know where I am, certainly if the market was set up properly, that can say, just come up and say, hey Donna you might not want to buy high sodium soup today. Try this, or this brand. So we're getting this continuous digital footprint, these records of our behavior in context in real- time. This data is detailed, rich, longitudinal, continuous and contextualized and otherwise. In other words, we know where it happens. So these profound technologies, as Weiser said in 1991 already, "The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it." And that is what's happening. So to recap game changer is this real-time and continuous temporally dense data that we get from new technologies. These new mobile technologies offer an unprecedented opportunity to capture the ongoing dynamic nature of our behavior. They give us a glimpse into these feedback loops, this dynamic thing that is the way that we behave and we can use emerging system modeling techniques to build really precise models for the first time, precise mathematical models of how behavior works, and it can describe and predict and improve health behaviors for individuals and groups in real time. So I just wanted to point out to you this is something that has both NIH, NSF and the European Union's attention because last year we were lucky enough to be able to have an international workshop on using new computationally enabled theoretical models to support healthy behavior change and maintenance. And at that workshop we talked about how difficult it is getting all this data together to know which data is trapped, to know how to piece it together and how to analyze it, and it's really a multi-transdisciplinary effort between computer sciences, human-computer interaction, user-centered design, behavioral scientists, health scientists, the whole 9 yards. We had many, many people there. Game designers, all these people coming to play, trying to understand behavior using new technologies. So what are the key questions that I ask from my point of view as a behaviorist and I'm going to use obesity as an example because it might have already been obvious to you but I am an obesity researcher. So when you want to intervene to change behavior in obesity, for instance, one of the major questions you have to ask is which populations do I want to study because behaviors differ across populations, they differ across age groups, they differ across ethnicities and social economic status, just to name a few things that really change people's behavior so if I want to change obesity related behaviors such as diet or physical activity, these are all very much culturally determined and age related. Another thing you have to decide is which battles are you going to pick, which behaviors are related to obesity using evidence, and which behaviors do you want to change. Currently there's a lot of attention to changing multiple behaviors at one time but you can't change them all, so you have to choose behaviors that fit together so we know that if we get people to exercise we know from Bonnie Springs work that they will also decrease their sodium intake. So which behaviors are related to obesity -- diet, physical activity. sedentary, incidence of sleep and there are others that come to mind. So make your choices when intervening, and which determinants are related to these behaviors. So I showed you a slide early on, showing you a bunch of determinants of behavior. So, for instance, the self- determination theory would say that motivation, intrinsic motivation, is a very important part of physical activity. You have to choose a model that's at least going to give you a start for thinking about behavior and the determinants of behavior as a starting point for developing an intervention. Now when you're using M health technologist to intervene you need to choose measures and that really is a collaboration between behaviorists and all forms of engineering and computer science, because there are many different measures out there and they are all going to give you different things, and it depends on how good you want that measure, so depending on intervention that you want, for instance, if I want to precisely know what your activity levels are at all times of day, I'm going to give you one of the high grade, research grade, accelerometers to wear. But if I just want to know your steps, or if I really just want to use a monitor like a step monitor as a motivational tool, I might give you something different. And there are two things to think about when measuring behavior. Everybody lies in a way, partially because we don't remember there's also desirability bias, social bias. So if I ask my daughter how much physical activity she got yesterday she might tell me more than she really got because she knows it's important to me. People are embarrassed about how much they eat but they also don't remember. So if you can use measures that are ubiquitous, that are objective sensors, for instance, that don't require any input from the people you're working with, you're going to get better or more accurate measures in one way. But in another way, people's opinions and thoughts and dreams really matter. So for instance I can give you salivary cortisol to tell you about my stress levels, and we've shown in our lab that salivary cortisol and perceived stress are correlated, but not that tightly. If you're doing research on stress your also going want to know what perceived stress is and the only way to do that is ask me. So a combination of ubiquitous and self-report measures, a combination of objective and self-report is usually what we're looking for. So when you think about measuring behavior you have to think about where can you measure ubiquitously, where can you use sensors, what do you want on-demand? So we call ecological momentary assessment an on demand measure. Ecological momentary assessment is usually a series of questionnaires that can be beamed to your phone. Now these questionnaires have to be small to fit into the screen and can be timed in many different ways. So, for instance, let's say there we're studying diet and stress. You can know about what time I have a meal and so you could at certain times decide to send me a question, a brief question. For instance, it's about dinnertime and you send me a text and say are you eating, what are you eating, who are you with, something like that. Time-dependent. EMA can also be event dependent, so if I'm wearing one of those detectors of eating. you can know that sends data to my phone and to the cloud and that can trigger a prompt, ecological momentary assessment to say, I know you're eating, what you eating or who you with, or whatever you want to know. are you stressed and you can also just send them at random times during the day. So there is a lot of different ways to use ecological momentary assessment to get subjective input when you need it, and then further down the line when you want to be intervene, we call it EMI, electronic momentary intervention, is when you actually use those messages, push them out. So when you see me eating for instance that signal to the phone could push out a message saying maybe it's three o'clock in the morning, it's the middle of the night. Maybe now is not such a great time to eat. it's a possibility. So choosing technologies is also tough as the behaviorist I always need the advice of engineers. Technologies change all the time. There's many, many new technologies all the time and they did they give you so much data that can be interpreted and used in so many different ways, depending on the way data is analyzed, the kind of algorithms that are developed. So choosing technologies is never something to do by yourself but they should be chosen to match your hypotheses and the needs for your research. So where do you need a really good measure. Where can you measure less well, what measures are really important, what's the burden on your population, all things to think about. Validated measures. So using EMA, using real-time technologies. There's not a lot of validated measures out there yet, and that's something that the community really needs to work on but we are working from measures that have been validated as questionnaires and then moving them slowly, revamping them for ecological momentary assessment for use online or on your phone. So it's very important, I think, mission for he field. So behavior, like I said, is really complicated. It's not only complicated across time but when you start thinking about measuring it with new technologies and a combination of new technologies and self-report, you have to sort of think about what all you want to measure. So here's an example of capturing one behavior. Behaviors are a compilation of micro activities, and you can slice it many, many different ways. So I chose physical activity as an example. Physical activity is something you actually always doing even when you're sedentary which is not the same as the physical activity. I'm moving, sitting here, so measures of physical activity are continuous, very temporally dense, and we need,I think, subjective and objective measures of physical activity. If it's one of the main variables in our study, it's one of the main -- if the behavior you want to change, if you want increase physical activity, you have to understand that activity really deeply in the population your studying and these practices change all the time. So, accelerometer is going to give you gait, speed, types of movement, moderate to vigorous physical activity, duration. Measured well with a good accelerometer those signals can be re-analyzed through machine learning and also through other techniques to actually recognize various behaviors, and I'll show you that in a minute. Context like who are you with and where are you, that can be known to some extent using a phone. For instance, you can know where you are or wear a GPS logger and your phone can recognize other people's phones if they are a part of the study. You can know where and when through your phone. You can know a lot about the built environment through GIS layering that on. You might want to ask who you are with. That can be triggered right after a bout of activity. So mediators. You want to know emotion, cognition, motivation. self efficacy, needs and desires, any of those mediators that you choose need to be measured and need to be measured well. Can you measure those ubiquitously, do you need to ask. And then moderators, gender, ethnicity, social economic status, education, all of these are things that you need to know just to measure one behavior that turns out to be this compilation of micro activities, sedentary behavior, light moderate, vigorous behavior, certain activities playing Wii or something. All of these different things you might want to know. Where you are, who you are with, what time of day. So who will utilize expertise in behavior and behavior change. Who needs me to join them when doing this kind of work: engineers, computer scientists and game developers. I work with them very closely. Modelers who are building new models of behavior need to work with behaviorists to develop these models. It's not something that we can do alone anymore. Public health researchers, doctors, interventionists looking to understand principles of behavior and behavior change. These are all people that would need a behaviorist on the team. So why avail yourself of expertise in behavior and behavior change. So new technologies are just widgets. They're great, but they're not going to make a difference, all by themselves. You need somebody to help you decide which behaviors to measure, how to measure them, and what might lead to behavior change. So we have to understand particular populations, how best to measure the behavior of interest within that populations and then we have to have targeted, tailored, iterative participatory design, and I'm going to show you an example of that in a moment. We have to know how to motivate people to want to give us their data all the time so if I want people to give me ongoing real-time data, if I want them to answer my prompts if I want them to play my games, if I want them to wear my gadgets I have to know how to motivate them to do that. I have to know what incentives have to be in place and I have to see how that works in a diverse population. So we have to understand how does this new technology brings about its effect or how best to develop, design and deploy technology that will add to our fundamental knowledge of behavior. So I'm going to give you a case study now, one of the studies that we did in our lab, of 'knowme' networks. Now knowme networks is a suite of mobile Bluetooth enabled wireless and wearable sensors, I'll show you that in a minute, that interface with a mobile phone and a secure server to process data in real time and this suite was designed specifically for minorities, for overweight minorities. We worked with them from beginning to end. This is a picture of the knowme networks suite. So knowme networks was developed with a deep, deep user centered design. So we started out by talking to the kids, asking them what would you wear if we wanted you to wear a monitor of some kind. At the time, this was a couple of years ago and all these devices were a lot clunkier so much larger and I thought for sure if we can give them some kind of a bracelet or some kind of jewelry they would love that, but no they didn't want anything that you could see. They didn't mind wearing an accelerometer on the hip but they didn't want to wear anything on their hands and they were perfectly happy to wear a chest strap. They were perfectly happy to wear a chest strap and that was very surprising to us. So we designed, we found off-the-shelf different technologies to put together, designed the suite, tried it out on them and let them play with it in the lab to make sure that they would wear it. Every time they gave us feedback we would adjust it and monitor. We had an advisory committee of about 20 overweight kids, Hispanic kids, that we were working with throughout the entire study. They would come in at various intervals in various combinations to help us with the technology. We also asked them a lot of questions about where they lived, what was important to them, what kind of messages we can give them that they would be interested in hearing from us. And every time we spoke with them, we iterated again on our technology on our ways of getting out messages on the on the way that it looked so the interface that what they're going to see how they wanted their data that's a really important question to ask. So interestingly, the kids we were working with wanted to see how much activity they had done, and they wanted to see that on their phones, but their parents wanted to see caloric expenditure and how many calories they could still eat during the day to balance out how much physical activity they had versus how many calories they had eaten to sort of reach their weight goals. Completely different interface desired by these two different groups. So here is the knowme system. They wore a chest strap with a heart rate monitor and a belt with an accelerometer and it was all hooked up to the phone and the data went up and was encrypted and we would crunch some of it on the phone and some of it on the backend back to the phone and we could watch the kids during the days they wore knowme and on the backend and I'll show you what that means. But first we wanted to recognize behaviors because we wanted to be able to cue them in when behaviors changed. So we did some pattern recognition. We brought kids into our lab for four different sessions and in each of these sessions they ran to this protocol of nine activities. The would lie down, they sat still, they sat and fidgeted, because I'm very interested in the NEAT, nonexercise activity thermogenesis. Non exercise activities thermogenesis, or NEAT, which is also a form of energy expenditure. They stood still, they stood and fidgeted, they stood and played Wii or any other kind of active game. They did a slow walk, a fast run and a regular one, and then did some free living activities. This was all in the lab, so we filmed them. We knew exactly what they were doing and then we used three sessions to develop algorithms and one sessions to test algorithms. This is a confusion matrix. If you've never seen one before. What you see on the this axis, the vertical axis, is actual behaviors so on the bottom is running very fast. And what you see across is the behaviors of the algorithms you're guessing and what you want is you want all the numbers to fall in that diagonal so you want all the numbers to fall along the black diagonal. What you see here is that we weren't very good at guessing the fidgeting, that was not something we can guess so well but when you collapse those two categories: sitting and fidgeting and standing and fidgeting and standing. We could guess behavior with about 94% accuracy, which is pretty good. Now when you're guessing behavior you'd better be accurate, especially if you're using it with kids because if you're going to share data with your participants you need to be right. If you're going to say you've been sedentary for last two hours, you better be right because if you're not, they are going to lose interest, you're going to lose them,. Children especially are really used to these very slick interfaces and very slick games and they expect the stuff to work. So knowme, a great idea, we're doing some great behavior recognition pattern recognition but will they wear it? So we took it out of lab, we took it out for a little trot in a free living out-of-lab feasibility study. We had twelve overweight Hispanic youth, and we brought knowme to them in a little suitcase. The knowme suitcase they really liked. So we came to their house and home and at that the time the knowme set up, if you remember, was a heart rate monitor on a strap, an accelerometer on a belt, a phone with pocket and both heart rate monitor and the phone had batteries, so they had chargers. The phone running knowme with so, took up so much energy on the phone that it didn't have a full day charge, so they had to change batteries, part way through the day. It also had a strip because what you find in the homes of the kids that we were working with was that there weren't many outlets. So that is something you really have to know. So we brought the knowme and we asked them to wear it for a weekend. We monitored them remotely and I'll show you what that looks like in a minute and we troubleshot via text message so we can see if they're wearing their devices and if the devices were on and hooked up properly. We could see that, and because we had personal algorithms we could pretty much know if it was actually them wearing it and the results were kids wore knowme for about 11 hours per day, whereas the battery life of the phone was only about nine hours. So that means they're willing to change the batteries and they sent us about eight messages and we sent them about nine just messages in the morning. Okay I'm getting up, and then we would have watchers on the backend. So on the backend were my PhD students and staff assigned to whichever kid they were watching for the weekend that were in contact with them over their mobile phones. So great. They're willing to use it and here is what we could see on the backend. This is that self fulfilling pie chart that shows you all of the activities that we were guessing those nine activities and every 10 minutes the knowme phone would collect the data from sensors and send it up to the cloud and would repopulate so we could see during the day what kind of activity they were doing. This probably looks like nothing to you for us was great because we can see, this shows all the devices and see if they are on we can see if they are working, see if there charged, we can see if they are hooked up properly, and we can see if they are wearing them. So this is very very handy and we could prompt them if something was wrong, if the heart rate monitor was on, and for this little pilot study will only had one drive-by change out where we had to change one of the monitors, so it went pretty well. We're happy with it. We thought, let's try now to reduce sedentary behavior, but in real time, because the kids that I work with are profoundly sedentary, 3 to 5 minutes of vigorous activity a day, maybe 20 minutes of moderate if we're lucky. So we thought, low hanging fruit. Let's try and reduce sedentary behavior in real time. Now the beauty of working in real time is that you can catch people before they do a behavior that's really bad for them, and also people learn better when corrected at the moment. So that's one of the principles from education is that the student gets real-time feedback, gets feedback right away for what they're doing. They are going to learn faster and better. So this is our interface. This is what our kids saw on the screen of their mobile phone that they develop themselves. This is what they wanted to see, through many focus groups. So you can see that this is a sedentary time dial and it goes up to about two hours and indeed at two hours we're going to start beeping you. That bar, the green bar on top, is a physical activity bar. If you keep that bar green 10 minutes of physical activity doesn't have to be consecutive. If you can keep that bar green, when you get up to two hours of sedentary minutes it will just reset on the backend you will hear from us. So the game is if you can keep that bar green, we won't bother you. So if the kids were sedentary for two hours, they heard from us. Knowme, the app generated a move message and we were alerted on the backend. The research team members send SMS message to the prompt to the participant to be active. And here is a really important part. We did really intensive intakes with these kids before we started knowme. So remember, we're working with disadvantaged children, some of them living in dangerous neighborhoods. You want to know what's around them. Did they have parks around them, are they dangerous. You want to know a little bit about their house. Do you have steps or stairs? Can we give you bands or some kind of hula hoops or something that you can use in your house to get exercise. So we knew quite a bit about them so that we be giving them the right messages. Each message was sent tailored to the participant. Here is an example prompt, I can see that you have been sitting for a while. How about doing some exercise. You can even jump rope while watching TV. So here's the design of this little study. The kids wore accelerometers for three days for baseline to get a baseline idea of how much activity.they got, and two weeks later, about two weeks later they wore knowme for three days. This was 10 kids. They were their own control. They were 97 percentile BMI, this is profoundly obese. This is quite obese, 97th percentile and 50% female and all of them Hispanic. So here's what we accomplished this is a small study so I'm going to cut myself some slack and say that P value .1 is okay. We managed to decrease sedentary behavior by about 200 minutes. That's really fabulous. The next question was, did the SMS prompts sent by the research team matter. Was the increase in physical activity or the decrease in sedentary behavior following that prompt or was just because they were wearing knowme and they knew that we were going to prompt them, and was that the active ingredient because if we don't have to send them this prompt it's going to be a much cheaper intervention if just wearing it. Maybe it's just the novelty. So we used cross lagged mixed regression analysis to look at that. So what we did was we collapsed activity measures into 10 minute segments and we looked at prompts, What if I prompted you now, 10 minutes later, are you more active. That was the question, and certainly that turned out to be true. Accelerometer counts were over 1000 counts higher in that segment after a prompt as compared to segments that were not after a prompt. So moderate activity measured by knowme was over three seconds higher than when no SMS count was sent. Now, remember, these were 10 minute segments all through the day so if you add that up, that actually does matter. And sedentary activity as measured by knowme was 10 seconds lower when no prompt, first after a prompt than versus when no prompt was sent. So it matters, what this tells us is that sending a prompt really does matter. Something I want to tell you about the study, which is kind of interesting, so we had humans in the loop. We have watchers we did not have automated messages here for a lot of reasons. One of them being the kids really didn't want it when you talk them in focus groups. They really wanted somebody paying attention to them on the line. What that meant was, we had lots of messages from these kids. They knew who was watching them and they had one watcher, about 33 email text messages from the kids about 43 from us, and those text messages I will show you in a minute, ranged all over the place, but here's one. For instance, here's one exchange that I thought was pretty funny. So this child hits the two-hour mark, it's been two hours of sedentary and they get a message from knowme and a message from their watchers saying, hey you have been sedentary for two hours maybe you should do something about that and the kid texts back, I'm at my grandmother's having dinner can you just reset on the backend. I can't get up from the table now. Okay. A few minutes later the child texts to the watcher, I'm really bored what are you doing? So they sort of like having us there to talk to, lots of text messages. The next question we had was not only now we know that the messages matter, that it matters to send a message, but is there a kind of message that is more effective so we use motivational interviewing framework to understand the messages that we sent and to see if type of message mattered. So for those of you. I'm sure many of you are familiar with motivational interviewing, it's a client centered directive method for enhancing intrinsic motivation to change behavior by exploring and resolving any ambivalence to behavior change. The spirit of motivational interviewing is really collaborative. It's coming alongside its evocative and it supports patient autonomy. So we had four codes. These four codes to sort of bucket in different messages that we sent to our participants. There a lot of neutral messages. Most of our messages were neutral because your devices are disconnected. No answering text messages like I'm bored. what are you doing. There were also affirmation text messages like great, that's good exercise. Keep up the good work if they been active. Suggestions like maybe you need to do something more active like going for a walk or dancing or doing jumping jacks. And then there were the prompting questions hey, it looks like you have been sitting around. Can you do something more active. So prompting the kids, very different kinds of messages. This is the distribution of messages as you see that 26% of them were these neutral messages about 13% prompts where affirmative 12%, prompting questions and 7%, suggesting messages. So those turned out to be more difficult for our team to generate. People are much more likely to give advice than to try to elicit responses from the child. Interesting to know. And what did we find? Affirmation really mattered so kids in the 10 minute segment after affirmation kids tended to be much more active than the 10 minute segment after no messages, but this is also more active as compared to other messages. So the prompting SMS worked also really well so when we prompted the kids, do you think you could get up now, accelerometer counts went up. No other message categories are significant associated so if you go back, the affirming and the prompting messages were the most successful and suggestion messages which I kind of put my money on, were not. Interesting. So conclusions, something that we ran up against during knowme, and we run up against again and again in all my other work is that technology moves more quickly than research so you really need to rethink our research designs, how we move forward. Also how we share algorithms how we how deal with the backend of technology because things move so quickly that by the time I got everything set up on a certain technology and all algorithms ready, my phones are outdated, the heart rate monitor company might've gone out of business. So how are we going to become more agile in mobile health to make our research move forward more quickly so we can keep up with technology. I think it's extremely important. I think it's pretty helpful pilot results for a tough chore. One of the kids said it's like having a doctor in your pocket. I think this is great, but it also means we have to rethink or think about where is that sweet point for automation versus having a human in the loop. This is not scalable and intervention like this where there's so much intervention from a human being doesn't scale up full population wide, so there has got to be a sweet spot, and where that is, that's a really important research question. We don't yet know how long behavior change will last with new technologies since you feel something we're just investigating, but I think it's one of the major research questions and we have to ask how long will behavior change last, how long do people want these interventions? How long are they willing to wear all these technologies and actually as technology becomes more and more ubiquitous, maybe we don't need them to wear anything. Maybe we just need them to have their phone nearby. The question is, how can it be made sustainable so is there a technology that can be deployed everywhere. I think that's happening now. So how do we sustain the research and how do we sustain that data exchange and keep these interventions maybe going to help people change and maintain behavior change, because what we have seen across the board is changing behavior, it's not impossible. It's not super, super easy, but it happens. We're pretty good at changing behavior. We are not good at maintaining behavior change. So how can we use new technologies to sustain behavior change and how can we make these interventions sustainable. I already talked about where's that sweet spot between automation and a human in the loop and maybe a robot can help us there. I don't know. And some next steps, are we developing systems of new capabilities geared for long-term wear, full adaptive trials and giving us, finding new ways for people to give us their data quickly and without too much disruption. Our continuous digital footprints record our behavior in context and in real time. They give us this rich and deep and detailed temporally dense data that I've been talking to you about. So we need to move M health and behavioral health into the 21st century. By integrating hardware sensor sets, by agility and partnership across different disciplines. By having really agile user interfaces, by deployment and data mining that we share across different platforms. So, to reiterate, we are surrounded by new technologies, Web wearable phone carry-able, placeable technologies that are going to help us to gather data to change our understanding of behavior to develop real-time theories of ongoing behavior using new computational models to give us testable theories of dynamic behavior. So these are models, this is just a mockup of the models we have to build that are dynamic, that add and subtract variables as we go, that turn on their heads. This is the way that we need to start looking at behavior, and I would like now to thank all the people that made my work possible. The knowme team and our funders, our workshop participants. The workshop I told you about and the workshop funders. The NSF, the EC and the National Institutes of Health. I hope you enjoyed this. I've enjoyed speaking with you. Thank you

Contents

Types

Health interventions may be run by a variety of organizations, including health departments and private organizations. Such interventions can operate at various scales, such as on a global, country, or community level. The whole population can be reached via websites, audio/video messages and other mass media, or specific groups can be affected by administrative action, such as increasing the provision of healthy food at schools.

Screening

Screening refers to the practice of testing a set of individuals who meet a certain criteria (such as age, sex, or sexual activity) for a disease or disorder. Many forms of screening are public health interventions. For example, mothers are routinely screened for HIV and Hepatitis B during pregnancy. Detection during pregnancy can prevent maternal transmission of the disease during childbirth.[7]

Vaccination

Vaccination programs are one of the most effective and common types of public health interventions. Typically programs may be in the form of recommendations or run by governmental health departments or nationalised health care systems. For instance, in the U.S., the Center for Disease Control decides on a vaccination schedule, and most private health insurers cover these vaccinations. In the UK, the NHS both decides and implements vaccination protocols. NGOs also may be involved in funding or implementing vaccination programs; for instance Bill and Melinda Gates Foundation assists governments in Pakistan, Nigeria and Afghanistan with the administration of polio vaccination.[8]

Supplementation

Supplementation of food or water of nutrients can reduce vitamin deficiency and other diseases. Supplementation may be required by law or voluntary. Some examples of interventions include:

Behavioural

Interventions intended to change the behaviour of individuals can be especially challenging. One such form is health promotion, where education and media may be used to promote healthy behaviours, such as eating healthy foods (to prevent obesity), using condoms (to prevent the transmission of STDs), or stopping open defecation in developing countries (see for example in India the campaign Swachh Bharat mission).

The use of laws to criminalise certain behaviours can also be considered a public health intervention, such as mandatory vaccination programs[13][14] and criminalisation of HIV transmission.[15][16][17] However, such measures are typically controversial, particularly in the case of HIV criminalisation where there is evidence it may be counter productive.[15][16][17] Laws which tax certain unhealthy products may also be effective, although also not without controversy, and are sometimes called a "sin tax". Examples include the taxation of tobacco products in the U.S. and New Zealand,[18] and sugared drinks in the UK.[19]

Evaluating efficacy

Evaluating and predicting the efficacy of a public health intervention, as well as calculating cost effectiveness, is essential. An intervention should ideally lower morbidity and mortality. Several systematic protocols exist to assist developing such interventions, such as Intervention Mapping.[20]

See also

References

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  2. ^ Kürüm, Esra; Warren, Joshua L.; Schuck-Paim, Cynthia; Lustig, Roger; Lewnard, Joseph A.; Fuentes, Rodrigo; Bruhn, Christian A. W.; Taylor, Robert J.; Simonsen, Lone (2017-07-31). "Bayesian model averaging with change points to assess the impact of vaccination and public health interventions". Epidemiology. 28 (6): 889–897. doi:10.1097/EDE.0000000000000719. PMC 5617796. PMID 28767518.
  3. ^ Bhuyan, Soumitra S.; Chandak, Aastha; Smith, Patti; Carlton, Erik L.; Duncan, Kenric; Gentry, Daniel (2015-08-07). "Integration of public health and primary care: A systematic review of the current literature in primary care physician mediated childhood obesity interventions". Obesity Research and Clinical Practice. 9 (6): 539–552. doi:10.1016/J.ORCP.2015.07.005. PMID 26259684.
  4. ^ Todd, Adam; Moore, Helen J.; Husband, Andrew K.; Bambra, Clare; Kasim, Adetayo; Sniehotta, Falko F.; Steed, Liz; Summerbell, Carolyn D. (2014-08-22). "Community pharmacy interventions for public health priorities: protocol for a systematic review of community pharmacy-delivered smoking, alcohol and weight management interventions". Systematic Reviews. 3: 93. doi:10.1186/2046-4053-3-93. PMC 4145162. PMID 25145710.
  5. ^ M, Schrappe; K, Lauterbach (1998-01-01). "Systematic review on the cost-effectiveness of public health interventions for HIV prevention in industrialized countries". AIDS. 12 Suppl A: S231–8. PMID 9633007.
  6. ^ Patil, Rajan R. (2013-11-01). "Application of PHEL - 'Public Health Epidemiological Logic' of Public Health Intervention and Public Health Impact". International Journal of Preventive Medicine. 4 (11): 1331–1336. PMC 3883261. PMID 24404371.
  7. ^ "Screening for hepatitis B, HIV and syphilis". www.nhs.uk. 2018-01-10. Retrieved 2018-06-09.
  8. ^ "Polio". Bill & Melinda Gates Foundation. Retrieved 2018-06-09.
  9. ^ Hetzel, B. (1983-11-12). "Iodine Deficiency Disorders (Idd) and Their Eradication". The Lancet. 322 (8359): 1126–1129. doi:10.1016/S0140-6736(83)90636-0. ISSN 0140-6736. PMID 6138653.
  10. ^ Honein, Margaret A. (2001-06-20). "Impact of Folic Acid Fortification of the US Food Supply on the Occurrence of Neural Tube Defects". JAMA. 285 (23): 2981–6. doi:10.1001/jama.285.23.2981. ISSN 0098-7484. PMID 11410096.
  11. ^ Frieden, Thomas R. (April 2010). "A Framework for Public Health Action: The Health Impact Pyramid". American Journal of Public Health. 100 (4): 590–595. doi:10.2105/ajph.2009.185652. ISSN 0090-0036. PMC 2836340. PMID 20167880.
  12. ^ Hiligsmann, Mickael; Neuprez, Audrey; Buckinx, Fanny; Locquet, Médéa; Reginster, Jean-Yves (December 2017). "A scoping review of the public health impact of vitamin D-fortified dairy products for fracture prevention". Archives of Osteoporosis. 12 (1): 57. doi:10.1007/s11657-017-0352-1. ISSN 1862-3514. PMC 5486688. PMID 28634891.
  13. ^ Haverkate, M; D’Ancona, F; Giambi, C; Johansen, K; Lopalco, P L; Cozza, V; Appelgren, E; on behalf of the VENICE project gat, collective (2012-05-31). "Mandatory and recommended vaccination in the EU, Iceland and Norway: results of the VENICE 2010 survey on the ways of implementing national vaccination programmes" (PDF). Eurosurveillance. 17 (22). doi:10.2807/ese.17.22.20183-en. ISSN 1560-7917. PMID 22687916.
  14. ^ Vamos, Cheryl A.; McDermott, Robert J.; Daley, Ellen M. (June 2008). "The HPV Vaccine: Framing the Arguments FOR and AGAINST Mandatory Vaccination of All Middle School Girls". Journal of School Health. 78 (6): 302–309. doi:10.1111/j.1746-1561.2008.00306.x. ISSN 0022-4391. PMID 18489462.
  15. ^ a b Burris, Scott (2008-08-06). "The Case Against Criminalization of HIV Transmission". JAMA. 300 (5): 578–81. doi:10.1001/jama.300.5.578. ISSN 0098-7484. PMID 18677032.
  16. ^ a b Cameron, E (December 2009). "Criminalization of HIV transmission: poor public health policy". HIV/AIDS Policy & Law Review. 14 (2): 1, 63–75. ISSN 1712-624X. PMID 20225503.
  17. ^ a b Mykhalovskiy, Eric (2011-09-01). "The problem of "significant risk": Exploring the public health impact of criminalizing HIV non-disclosure". Social Science & Medicine. 73 (5): 668–675. doi:10.1016/j.socscimed.2011.06.051. ISSN 0277-9536. PMID 21835524.
  18. ^ Wilson, Nick; Blakely, Tony; Tobias, Martin (2006-11-02). "What potential has tobacco control for reducing health inequalities? The New Zealand situation". International Journal for Equity in Health. 5 (1): 14. doi:10.1186/1475-9276-5-14. PMC 1654162. PMID 17081299.
  19. ^ Briggs, Adam D. M.; Mytton, Oliver T.; Kehlbacher, Ariane; Tiffin, Richard; Rayner, Mike; Scarborough, Peter (2013-10-31). "Overall and income specific effect on prevalence of overweight and obesity of 20% sugar sweetened drink tax in UK: econometric and comparative risk assessment modelling study". BMJ. 347: f6189. doi:10.1136/bmj.f6189. ISSN 1756-1833. PMC 3814405. PMID 24179043.
  20. ^ Bartholomew, L. K., Parcel, G. S., Kok, G., Gottlieb, N. H., & Fernández, M.E., 2011. Planning health promotion programs; an Intervention Mapping approach, 3rd Ed. San Francisco, CA: Jossey-Bass.
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