I play video games because I just want to be swept away. And long before I had my first home computer, the Amstrad CPC, I was actively involved in playing the arcade games, whilst other family members went outside and interacted with people. Talk about stranger danger. Up until the arcades, I think I remember roaming the streets, armed with nothing but sticks and boundless imagination. We pretended to be soldiers, cowboys, and who didn't love British Bulldog? It's as though trial and error was the name of the game. Then I found video games, and it all went downhill from there on in. Good luck! You'll need it! So let's go back to the Dark Ages, 1982, to be precise. Only to set the scene, one of the marvels of 1982. It was the birth of the world's first-ever CD player. And let's not forget the infamous ZX Spectrum. But it wasn't all cutting-edge technology back then. 1982 also brought us the Cabbage Patch Kids. What?  Come on, you loved it really. But the reason we're here, and let's not forget, 1982 brought us Robotron. And if you rubbish at maths, that's four decades ago. Back in the day, it was a thing of beauty to watch a skilled Robotron player in high gear. Two joysticks against the globe. All the enemies are present and engaged in combat right away. It's definitely not one of those games where you think first and shoot later. Instincts and going with your gut are the wisest course of action. In fact, if you're going to beat Robotron or achieve a high score, you need to enter a hyperkinetic state of concentration. Robotron was the third game that Eugene Jarvis and Larry DeMar, design team, gave to Williams. With the exception of a science fiction plot, it was a complete departure from their work on Defender and Defender 2. The story goes that Jarvis' right hand was broken as a result of a car accident. Apparently, he developed the concept for the Robotron controls as two independent joysticks while he was recovering. So get this, Jarvis claimed that he drew influence from two earlier video games. The first game being on the Commodore PET called Chase. It was a character star game where you could turn asterisks into pie signs. Then there was Stem's Berserk, which served as an additional inspiration. Despite the fact that you could only shoot in the direction you were travelling, Jarvis always liked Berserk. He fixed it by using one joystick for movement and one joystick for firing, which gave players an additional level of freedom. Here's Larry DeMar talking about the controls. Controls were totally one of a kind at the time and they've hardly been used ever since. Eugene really wanted to give you that monster control and one of the really adrenaline rushes in the game as a result of the amount of power you have.  But Stem offered more inspiration than just that. Within a few months they were supposed to deliver frenzy, the follow-up to Berserk, in order for Williams to have the upcoming release of the next new robot game. The Robotron timeframe was significantly accelerated. The majority of the programming was accomplished in an astounding four days after the first concept was developed. They just kept adding robots. If 20 robots were fun, why not 60? If 60 were fun, why not 120? Soon they brought to life a seething mass of enemies. 40 years later, few games have equaled Robotron's body count per second. The issue now is that they required a narrative to explain all of this senseless carnage so they made the decision to include the nuclear family. Defender proved that saving people works and it started to take a theme. Family members became your friends, introducing good guys and terrible guys to the Robotron universe. A standard science fiction scenario was beginning to take shape. Jarvis reasoned to himself that if machines kept getting smarter, they would eventually realize humans were a liability.  They're trying to blow up things, they've got car bombers, they've got nuclear terrorists. All these mass murderers, all these nasty little habits that humans have.   In this darkly twisted techno dystopia, machines have slowly clawed their way back into society. Only now they've got rights, similar to those of their human counterpart but the power they hold is no mere convenience, it's a lethal entanglement. Unplugging one is no different from committing murder. Amidst the mechanical mayhem, robots grow disdainful of humanity, deeming us a wasteful species. Resources that could churn out more of their own kind are squandered on us fragile beings. They hatch a cold, calculated conclusion that humans must be eradicated. And then, like a renegade glitch in the system, enter the player his mission, destroy the robots and save the nuclear family. Robotron burst onto the sea. It was an instant hit, offering both fun and innovation. Its claim to fame, being the first game with 2D Blitter hardware. Robotron continued improvements to William's standard electronic system. Acknowledged to be the best in the industry at the time, backed up by an excellent service department. It shipped a healthy 19,000 units and spun off a cabaret version with a 13 inch monitor and cocktail sit-down model. Marquis, reading simply, 2084 were created, but when the final version of the game was released, the title was officially Robotron 2084. It was Jarvis and DeMar, the dynamic duo, who birthed three games for Williams. With Blaster being their final creation, it went through a short lived test run, stashed in an enigmatic cylindrical black cabinet, just like Bubbles, its predecessor. Atari computers also housed its home incarnation, but alas, the notorious video game crash of 83 wrecked havoc on its fate. The brains behind Williams, including Jarvis and DeMar, took a sabbatical from the gaming world, leaving the mid-80s gems like Blaster to remain lost in the abyss of unfinished projects and abandoned tests. But fear not, for they would return in the late 80s, guiding the arcade industry's resurrection. As time marched on, the tales of these forgotten games from yesteryears echoed through the annals of gaming history. There were some games in that period that were produced, either low numbers or not at all, that most of the audience probably hasn't had a chance to see. Some low production games were Mystic Marathon, Turkey Shoot, Inferno. Those three were, they were tested, and there were some number built of each of those models, and they can be found in collector's hands, but the quantities are limited. Then there were games like Playball, Speedball. There's at least one other that were fully developed and tested, but were never put into production, and there are no games in collector's hands. I have the only existing Playball sitting in my office. Well, Robotron, like any other game, boasts some wicked bugs. In the initial versions, Quarks went by the catchy name Cuboids on the main attraction screen. Oh, and here's the fun part. During the Tank Wave, if the tanks fire 20 shots that don't hit anything, the game thinks there are still 20 shots in the air, and you won't fire anymore, and that's your chance to take them down. But wait, there's more. The Brain Wave has an epic bug that's a bonus point extravaganza. So let the bug hunting and point piling begin. What happens is the brains, their algorithm which was supposed to be seeking out the closest human, and then go and reprogram them. That was supposedly their algorithm. Something got screwed up, which actually I never really looked into it. Didn't really want to know, because it was working good. But they all turn out they seek, they initially will seek the first human, which in the case of the first Brain Wave is Mikey. There's one Mikey and there's all these other mommies. And so they're all going for Mikey. If you can keep Mikey alive, don't pick him up and don't allow the brains to kill him. If you can keep him alive, then the brains will continually try to seek him, and they won't program any of the other mommies. And so in that situation, you can basically kill all the brains, but you have to leave one left so that the wave doesn't end. And this is the horrible thing, is that so often you'll kill the last one, it'll be a grunt, it'll be left or something, and it'll run into an electrode before you've picked up this treasure trove of points, which is all your humans are just waiting out there, and you wait for one turn, you want to pick them all up in one turn so you can turn them all into 5,000s. But if you somehow blow it, then you get nothing. Perhaps the most interesting hidden feature is the screen, Credit Easter Egg, created in part to frustrate would-be game pirates. We also had a sequence where you can play with the controls and out comes a copyright message with Eugene's in my name on it. It's in both Stargate and Robotron. The Robotron sequence I know off the top of my head, the Stargate one I have written down. The Robotron one, each of the sequences are three combinations of buttons being pressed.  It's not difficult on a Robotron, it's much harder on the digital eclipse version, but I have done it. It doesn't matter how long you spend each combination, but when you let go of the combination, you have a quarter of a second to be on the next one. Combination one is move right, fire up, and press the player one start button. Combination two is move up, fire down, and press the player two start button, that is the two player start, and then combination three is move down, fire up, and you hold the fire up, up, and it'll hold the message, and you let go of the fire up, the message goes. Ah, look at DeMar, the Pimball Wizard turn programming prodigy, forever etching his name in the annals of code history. And then there's Jarvis, mastermind of video game Metamorphis. He transformed Robotron into Smash TV, then lurked kids to the arcade with cruising USA, thwarting any hope of them ever leaving to get an N64, and even now, Robotron still haunts Jarvis' thoughts, a nostalgic piece of his brilliant mind. Ah, the wonders of the gaming world. Here's Jarvis reminiscing about the wonders of Robotron.  I mean there's players that do five or ten million, unfortunately, but my personal thing is like to do a million, and I don't think I've quite achieved that even yet to this day. I've had like a 900,000, I've had several 900,000 games, and somehow at 900,000, like 40,000, I'll just get weak in the knees, and I just like, I won't have it, you know, and I'm just like, I'm watching helplessly, you know, I've just, you know, maybe I've had a run of 100,000 points on a previous guy, but some reason I'm just sitting here, and I'm helpless, and I'm crumpled, but I'm just like a sweaty, limp dish rag, and something that can't get over that million point barrier, but that's still my personal ambition.  Well, I hope you enjoyed. Don't forget to like, subscribe, ring that bell for future notifications of new videos. Please don't forget to share, and if you're feeling especially generous, please leave a thanks. Oh, I nearly forgot, and please, by all means, leave a comment. Let me know what you think. Right, I'm off to play some Robotron on the N64, one of my favourite arcade conversions. Until next time, bye!


I'm Faith Smith, the deputy director of Future Tense, and I'd like to thank you for joining us today. Future Tense is a partnership between Slate Magazine, New America, and Arizona State University. We look at exploring technologies and their impacts on policy in society. Today we're exploring mobile health innovation and the data security and privacy concerns raised by its use. We're excited to be joined by a really impressive group of experts across the fields of mobile health innovation and policy. Our first conversation will explore the wealth of technology and mobile health innovations that now allow us to identify, manage, and research elements in unprecedented ways. And we'll address the question of who owns this sensitive information. Few notes before we start, we will have time for questions at the end of each panel. If you have the opportunity to ask a question, wait for the mic, identify yourself, and please make it a question with a question mark at the end. Feel free to join the discussion on Twitter using the hashtag mobile health and by following at future tense now. So I'd like to invite our moderator and panel to take their seats on stage. Moderating our first panel, whose data is it anyway, is Sherwin C. He's a Vice President of Legal Affairs at Public Knowledge where he coordinates their work on copyright issues and analyzes their impact on domestic and international effects on IP and technology policy. So please join me in welcoming Sherwin and our first panel to get us started. Thank you. So as you heard, I'm a copyright lawyer, which is sort of an odd fit for me to be on this panel. The reason that I got interested in this stuff was because of, I was concerned about the ways in which copyright law, among other things, could possibly stand in the way of patients getting access to data generated by their implanted medical devices. So that leads us to this question, whose data is it anyway? And, but that's a much larger question than the ones I was asking. We have questions about who is the who. What sorts of patients or users are we talking about in what sorts of countries? Who are the other players who might claim ownership of this data? Would it be doctors? Would it be other healthcare providers? App developers? Or possibly insurance companies? And what sort of data are we talking about? Are we not talking just about electronic health records? Are we talking just about things coming off of devices? There's a whole universe of data that's being generated that can feed into all of these things. So have an extremely distinguished panel to discuss these issues. We have Joel Solonikio, who's CEO and co-founder of Magpie, an award-winning mobile data collection and messaging software with over 35,000 users in more than 170 countries. He's also the assistant professor at the Department of Pediatrics at Georgetown and consultant in social entrepreneurship, public and global health and using technology for development. We also have Sarah Watson at the end, who's a technology critic and a fellow at the Berkman Center for Internet and Society at Harvard University. There she contributes to the center's internet health data initiatives. Her work addresses how individuals are learning to live with, understand and interpret data, and her writings appeared in the Atlantic, wired the Harvard Business Review and Slate. And last but not least, we have Deborah Estrin, who's professor of computer science at Cornell Tech. She's also the founder of Healthier Life Hub and she directs the small data lab at Cornell Tech and also co-founded the non-profit startup OpenM Health. So thank you all. I just wanna take a quick minute and have each of you talk for a bit about sort of where your perspective is on this question. Whose data is it anyway and what are the issues that you're looking at? So Joel, can we start with you? Sure. Well, as you know, I've been working in public health for a long time, international health and for me, I guess I kind of grew up working for CDC, the Centers for Disease Control, collecting data about poor people and their health in developing countries. And initially on paper and then we've created Magpie to sort of speed that process up and do it electronically. But more recently, as Magpie's become more successful, we find ourselves to be effectively a repository of an enormous amount of health-related data belonging to who? Certainly controlled by us, it's on our servers, but for us, it's quite interesting to look at the different stakeholders or groups that are involved, whether it's the group that often referred to maybe euphemistically or optimistically as the beneficiaries, that is to say the poor people to whom the health data pertains, the groups that collected it, which might be the World Health Organization or CDC and of course us and society in general. And so we're quite interested in having a lot of conversations about what can be done with this data and how can we, for example, help the beneficiaries themselves to exert more control over what happens with their own data. And that's survey data gathered using Magpie and other sort of public health outreach? Well, it's not necessarily survey data, although that's a lot of it. Magpie is basically a mobile form system and so we have, it's used for non-health as well, but within the health sector, you might have people doing surveys, but you might also have people just collecting, using it in a sense as an electronic medical record system or registering patients at a facility. Just lots and lots of the data that's collected around the health system and the public health system is collected with Magpie. Thanks, and Sarah? Yeah, so I come at this from the perspective of how consumers and average users and kind of just the kind of citizenry should care about accessing their data. Not specifically mobile health data, but just data in general. But in looking at the quantified self-community as this kind of canary in a coal mine for kind of the emerging issues that we're going to face with policy and kind of legal access to information, into data, I was really looking at this as a kind of set of people who have concerns about whether or not they can access things, compare them to other data sets, whether or not they have the APIs that they want. So when you say quantified self, what does that mean? Sure, so quantified self, on the one hand, there's the kind of small definition of them, which is the kind of QS with the two cap, like capital Q, capital S. The Meetup Organization and the conference run by Gary Wolf and Kevin Kelly as the community of people who are doing kind of extreme level of quantifying their bodies, using sensor data, kind of accessing information. So like Fitbit data taken to an extreme level. Yes, exactly, but far beyond that, yeah. And a lot of it is kind of self experimentation as well. And so it's a fairly technical audience, but what they're running into as a problem and what kind of information they're interested in accessing, the information that they know is there, but they don't necessarily have access to it, is really what was kind of one of the things that came out of that research, so. Okay, Deborah? So just to follow up on that, my interests come both from bringing that QS-like behavior and interest, but to people who generally don't have time to sit there and study their data, but rather thinking about how that data is going to come forward and fuel apps and services. So whereas most people don't spend a lot of time, as we said, necessarily studying their data, we do have evidence that they do buy smartphones and download apps to those smartphones. And so the opportunity for those apps that people download, not just to be the source of data, but to be informed by the data that people generate so that our applications and services can be as personally targeted as our advertisements are, that the notion of recommender systems can come and recommend to me things from healthier habits to news items to when I should adhere to something. So what sort of examples of that sort of data? What sort of data is the user actively providing? What sort of is being passively collected? Where is it coming from? So I think the example of drawing on the FitBid analogy is a perfect place to start, because FitBid as a product and the others in that ilk are ones that measure your activity and tell you sort of how much have you walked today and how active are you trying to get people to be less sedentary. But that same information about your activity, which by the way you can capture with a software running on your mobile phone, can give you information that can inform somebody like somebody with rheumatoid arthritis that their mobility patterns, how much they move might be an early indication of a flare that they're going to experience. Similarly for somebody with Crohn's that as they stop leaving the house and spending as much time outside, that can be an early indicator or a signature for them of their disease worsening. Depression, anxiety, whole range of chronic conditions that people deal with. These same signals that we might look at in their raw form, you walked 10,000 steps today, are also the beginnings of things that are even more informative about a particular condition. So it sounds like this is like an application of outside expertise to what the sort of quantified self consumer might be already gathering. Is there a question, and I think I'm curious about all of your reactions to this. Is there an aspect of external control being applied to what was sort of a self control issue with an external company or maybe a development agency coming in and saying, well okay, here's this data, here's what you should do with it. Well even in the case of Fitbit, they don't really, aside from kind of putting the 10,000 steps a day mark on encouraging people to reach that goal, they're not doing a lot for kind of suggesting what healthy habits and what other kind of personal recommendations they should have, and that's in part because they're still just a fitness company and not a health company. And so I think that distinction that we're getting into about health data versus kind of fitness data and just data exhaust that is coming from mobile phones and sensors gets at this question of what kind of prescriptive information can we build out of that. I think also it's interesting because of course what you want to do is you want to incentivize Fitbit and other companies perhaps, depending on the business model to use that data and apply it in ways that are useful for us, that are useful for society, and at the same time recognizing that their interests may not always be the same as our interests. And so when you create some kind of incentivization system saying for example, if Fitbit creates an API and allows people to look at that data and then produce apps that many of us enjoy and like and tell us something useful about our health, what else are they doing with that? And again, what happens at that intersection where the interest of that company is not in fact my interest. Can I roll it back then? Can I get my data back? Is it my data? And can I try a minute? Oh please. And maybe to step back a little bit from terms that have more to do with ownership because it's so hard to figure out that ownership piece. I think that from a pragmatic perspective, I hope a battle we fight is that people have access to their data. So even if I can't literally get it back, but that I as an individual should have programmatic access to that data so an app could run over that. So that it's, and it's not a matter of, it's sort of API access application protocol interface that a program can get access to it, not that I can go to and get it through FOIA, right? And that how important it is that the data we generate should be available to individuals so that it can bring utility to individuals because we choose apps and services that are gonna run off. So you're not just talking about about somebody being able to prevent other people from using the data. It's more of just a question of accessing it. As a minimum, I think we need to give people access. It rebalances the power of even understanding what's out there about you and also will promote more utility in the space. I don't want to dismiss the importance of this question of third-party access to data and regulations and mechanisms that we should pursue, but I think we need to separate them just because that second one is much harder. Okay. It's also, I find that it's interesting, even our language, I mean it's difficult to even get language that we can use around this because when you say your phone, I mean it belongs to you, right? You have ownership over that phone. When we say your data, it doesn't necessarily imply ownership. I was at a conference yesterday I was mentioning and one of the photographers snapped a photo of me which some automated Google search showed me this morning online and I said, oh, that's a great photo. I want to get a copy of that photo. I emailed the photographer and they said, yes, you can purchase that at Getty Images. Yeah. I mean, it's my photo, but it's not my photo, right? So how do we even, you know, how do we even get a common set of vocabulary to talk about this? Well, and that gets into the kind of fundability and spread of where the state is going and the idea that ownership is, you know, something you is alienable and kind of exclusive is not the case in the kind of digital realm. Well, I mean, it is an incentive for people to want to be able to sell data, right? That's where a lot of the, it's being generated. So what are the incentives that people have to allow user access to the data? And how do we create those incentives? Or what are there that's out there that would encourage this to happen? So I think there are things that I aspire to the world to be that way and there's undoubtedly friction in there as well. So the opportunity to have an ecosystem of apps and services that run on our data, right? In the same way that apps run on our phone. If we had had that conversation, I guess it's only, only 10 years ago we might've had the conversation about what would incentivize the manufacturers of phones and the cellular providers from opening up our phones to us. We had those conversations. And it wasn't exactly clear, but we did get our access to our phones. The ability to download apps, the existence of the app store, not having what the apps that were on your phone be locked down and just provided to you when you went and bought your phone from your cellular provider. And a huge market has opened up that has further greatly increased the amount of traffic on those cellular networks and so forth. So I think we have the same opportunity to do that here and to appeal to people's ideas of growing markets. By and large, the vendors, they think about the value of their data and aggregate across all their customers. And you don't necessarily give that up by giving the individual access to their own data that can further drive other applications. So let me take a step back. I feel like maybe as the lawyer on the panel, I want to jump immediately to the, okay, what are the legal structures? What are the barriers? What can possibly go wrong here? But let's move away from that for a bit. So let's paint a picture of what we want the world to look like. What is the best case scenario for the use of access to this data that y'all can see? How does this work and how does it fit together? I think what we want is we want our data to solve problems for us. Like we want our apps and our phones to solve problems for us. So what are the problems we have?  And of course we now have a variety of tools to manage our money that we could probably not have imagined 10 years ago. I'm not sure if we really could say that we have, we've kind of reached the final stage with that and surely not with health applications. But certainly as a physician, I can see the potential for really being able to deliver to users as something of tremendous value, which is, for example, let's say just the one thing that plagues so many Americans, the ability to effectively lose weight.  I mean, that would be a heck of a problem to solve for everyone. So I would go with his one line statement, being able to have a range of apps and services that come and solve problems for individuals. And to do that in a way that we don't expect to be controlled by any one platform. And so there's a lot of interest and that's with my hat from the OpenM Health side is in having a community that creates the definitions of what are these useful pieces of data, how do you describe them? And it's really as we move up the information food chain from the individual step counts per day, from the individual measures of somebody's pain per day or tapping per day to something that becomes a clinically useful measure of them over the course of weeks and months because that's how you determine really how they're doing clinically daily things are still noisy, they're fantastic and it's over the course of the week and month that you start to see those trends that are gonna be clinically really meaningful. And so in order for us to be able to move the health system ahead and the apps and services ahead, we need to do a lot of shared learning and standardized ways of understanding those things. Just for example, HBA1C is the measure that's clinically used when they test your blood and see how much glucose has been in your blood over the course of the last few weeks or month. It's not your blood glucose level right now. And that's what's clinically used to figure out are you pre-diabetic, is your diabetes under control? And we need to be working through that system to move on to that level of understanding and that level of measures and we need to do that as an overall community. So in my ideal world, individuals can get their data back, they can share that data with clinical research. Somehow we've been up here for how long? No one's mentioned research yet. We can share our data with clinical research and we can move the state of understanding of how do we use continuous daily measures effectively in meaningful care? I think my answer has to do with a little bit more of the kind of everyday, how do I understand the data about me that is out there in the world and kind of acting on my behalf? So and that may be health data but it may be the full range of other data that we kind of throw off using our devices. So I think the question for me right now is whether or not we understand the full range of uses of data in any context, whether that health data then is used to improve health outcomes or improve profitability of insurance companies and claims and underwriting. So I think kind of balancing out that range of potential uses is the thing that needs a lot of work and trying to understand what those uses actually are in the broader sense of this larger data ecosystem that we're building. If I can follow up to that, I think we're starting to realize that with all these companies that are collecting our data off of our apps even without Fitbit just from our phone that we essentially have made a deal with these companies and often it's that deal, that contract document that we look through the terms of service that no one ever reads. And knowing, so we're making a deal, we're saying you can use my stuff, you can monitor me in exchange for something but many times you don't really know what that deal is and I think that's kind of what, it seems to be, that's what you're getting at. It's like knowing exactly what deal have I signed up for and how can I perhaps negotiate a better deal. Right and I think the thing that we're missing right now is often we have this deal between like, okay, me and Apple or me and Google and consumers and we all understand that but that secondary and tertiary uses of the data that comes out of those relationships is far less understood. And I think we don't really even have a full view of that institutionally, legally, yeah. So my instinct on this is to say, well if you don't, okay, you're not sure how it's going to be used. It could have all sorts of repercussions. We want to treat this like it's sensitive data. When people talk about health, they talk about data, well there's a clear sensitivity that's there but Sarah you've mentioned you were not just talking about health data, we're talking about fitness data which is something different. We're talking about a lot of things that people are themselves entering in and making a choice to disclose as well. So maybe the lines that we typically draw around these things aren't quite there. Obviously we want protections but what do we want those to look like and how are we dealing with this? I think the kind of traditional regimes that we have for addressing these things, HIPAA, that exists because we had a clear definition of what health data and sensitive health data was. I think the kind of world that we're jumping into in the context of big data and kind of using all these other proxies for behaviors and health using the sensitive data that's not really a health device and it's not regulated by the FDA is kind of blurring the lines of what that definition really is. And on the flip side, companies like Fitbit don't want to be judged as a health company because they want to get the thing out to consumers as fast as possible and the kind of process behind marking some pedometer as an FDA approved device is very onerous. So I think kind of figuring out how to better navigate that fuzzy line is something that we need to talk about. There's an interesting opportunity now also to recognize that if we do get the world where increasingly there are the Fitbit APIs and the other APIs, you can go to Gmail and get the API to all of your historical email that you've written. You think your health data is sensitive, what about that? And they open up that API to the individual and you can share that information that access with apps and services that are out there and people do it because they find it useful. But increasingly, as we were going to bring these different data streams together and it's my continuous and historical location and my email threads and these other things, the opportunity to build a space where consumers can run those apps and services over that data, that is sensitive data. And no individual actually has the resources to be as careful with the data as Google actually is. And so we're opening up on the one hand, I believe we need to give individuals access and the right of access to that data but we also wanna jump in there and give them ways in which they can have private places in the cloud that are secure and give them an opportunity to do useful things on top of that data. So I think there are some new market opportunities that are opening up as well and that really need to be addressed. I think it's also interesting because we're seeing those market opportunities addressed in different ways by different companies. There's kind of a growing, for example, kind of Apple, Google dichotomy where Google says we're gonna make money by doing stuff with your data and of course people want that to be clear and I think Google would say that they also want that to be clear to you and Apple saying well actually we just wanna sell you iPhones, we won't do anything with your data or we're gonna do very little with your data and sort of for the consumer trying to decide, well I guess deciding whether I should choose to go one way or the other really depends on what's the value you guys are providing to me.  Is that, cause if that's sufficiently better than the iPhone, I'll go with whatever Google's giving me. But again, it's again knowing what the deal is whereas right now it's so opaque in so many instances. So we do just another shout out for Research Kit in that regard because they did sort of up the level of saying you can walk people through a set of consenting processes and a legal framework in a way that's intuitive and obvious and I think that's an obvious to the person what it is that's trying to be communicated and I think that we can follow that lead in other places as well where we're asked to just check off opaque terms of service. Well this brings up just kind of another issue which is that if you work in medicine everyone in medicine is familiar with the concept that the so-called consent process in medicine is exactly as useful as the terms of service system and software that is to say the surgeon goes to talk to the patient, they've got a 17 page form, they say have a look at this and then let me know if you agree with it and the patient's like doc do you think I should get the surgery? Yeah I do, okay let me sign, right. I mean obviously that is not delivering the kind of understanding of the deal that we're talking about and in the terms of service it doesn't either. So I mean this is a whole separate issue. How can we communicate complicated ideas whether it's medical or research consent or just a software terms of service how do we communicate those ideas in a way that the consumer understands and I'm not quite sure how to solve that problem. Yeah, don't look to him, he's the lawyer. This is your fault. Oh, I'm more than aware of all the problems with what real consent is and what sort of understood as well we put it out there. Actually Joel you mentioned the difference between an Apple and a Google model just as a shorthand or you know well on the one hand Google's like okay we're gonna do things with your data and in exchange we'll give you these services but for free, usually. Apple charges a fair amount. So there's an interesting dynamic that happens there in terms of who gets to participate in which of these models. So do you have thoughts on that anyone? Well I think I mean Apple of course I think to say that Apple charges a considerable amount is a considerable understatement. I'm a fan of their stuff but certainly the folks that I deal with in poor countries certainly most of them don't have Apple devices and can't afford to have Apple devices but I don't think there's anything that says you couldn't have a similar business model at a lower price point. There's nothing that says that HTC or Huawei couldn't say like Apple we're not gonna do very much with your data. Instead we're just gonna make money on hardware. So it's possible you could extend that to others. It's complicated. Okay. It is complicated. So Debra one of the things that you mentioned earlier was so one of the things that we want to see is this opening up the way we saw mobile platforms open up and that seems to have happened through a combination of movement on the companies to recognize the need for third parties to happen. It also seemed to come from a consumer push to want to have control over devices and to want to have more flexibility to make what was a more specialized device and more general device. So what sort of what are the forms that you see people moving in to try and make this happen for medical mobile data? So I think it was until the vendors did it until people came up with business models that supported it in the app store and the play store and such. It didn't happen that consumers eagerly adopted it. It did, but consumers themselves couldn't make that happen. It really happened because the Googles and apples of the world found a way and the value in having the third parties develop all kinds of apps that would bring a lot more customers to the devices and things that they sell. And that worked actually for the most part everybody won through that process. So now in the context of health we do see and it's been going on for a long time increasing patient engagement began on the web, right? It began E-patient Dave and onward to patients like me and Inspire and all the online fora and blogs and web based conversations that people have where patients are increasingly taking an interest in one another as experts that somebody else with my particular condition and my demographic has a huge amount to share with me and not just my doctor. And so I think that that's where we're seeing that those organized cohorts and communities coming around and they will really make the research kits of the world really, really blossom. But you said it wasn't until the companies got there and those companies though were coming when we're talking about the phone analogy or coming in from outside of the telephone space. They were computer companies or internet companies that sort of jumped into this. Is this, does this mean that what we're looking for is somebody outside of the existing players now, medical advice companies, the tech companies that are doing this now, outside of those areas to bring something in? Is that what's necessary? The disruptors. Well, yes. Patients are the disruptors. The patients and the consumers and since, at least in this country, so many of us as consumers are not well because of the environments we live in, food and otherwise, that it's, I see it as a consumer to patient revolution and that the ones who are being, who will be disrupted are the docs, not you and her too. Thanks. So am I being too knee-jerk a policy lawyer when I ask is there a role for law and regulation in ensuring that this happens? Or is this something that you think will, that can grow out of the existing community and the existing activism? I think there's certainly a role to play in at least, at the very least, the kind of technical policy side, which is what are the standards? What does a step look like if you, even if you get the API access, how meaningful is it and how can it speak to other things, to other systems? So I think there's a lot of work to be done even on the technical side and that's probably going to come from, the places that usually come from standards bodies and kind of activist kind of technologists and so. And so to that, that's again, sorry, sales hat of open M Health and with the funding that we've had from the Robert Wood Johnson Foundation over the last few years to try to build a place where a community can create standards in the way that there was a community that helped to make the internet the huge commercial and open success that it is. But I strongly believe, I do believe in law and I do believe in the role of those things and where a lot of the focus needs to be on what are the rules around third party use and will there grow to be things like GINA that the equivalent of, so GINA around what can you be asked? What can people use your genomic information for? Can it be used to restrict you from access to insurance and things like that? We should be thinking about what can we be asked for? What can we be required to give in exchange for different types of services be it insurance or employment? And there will be lots of conversation about what that means. The extent to which HR can ask to know something about you and your past 10 years of behavior, there is no practical limit to how cheaply that can be done now. What's the right thing to be done and what kind of a society do we want to live in? And I think another issue is also in these negotiations that will take place in Congress and elsewhere to make these laws that regulate these interactions. The question is who will stand for the consumer, right? Or who will stand for the individuals? Who will stand for us? Is that going to be organizations like yours? Is it going to be philanthropy funded organizations? Is it going to be some other kind of a union of consumers? But the point is that there's an enormous disparity in resources between, say, Apple and Google and me. And so not counting on the benevolence of those organizations to represent my interests, that's not their job. The question is how will I even... I mean, I do this full time and I don't understand all these issues. The question is how's my mom going to get represented in that situation and who will represent her? What kind of an organization will arise to meet that need? I don't know the answer to that. And is it versus the Apples and Googles or is it about pharma and large hospital systems? It's just not clear who the other side is. Right. So possibly multiple fronts on which to advocate for consumers. So what else exists in this space that we haven't touched on? What have I missed? Is there anything sort of that sits out there that makes you think this is what people don't understand about what it is that I'm trying to do? I mean, from my perspective, I think the one thing that doesn't often come into these conversations is the idea that this has expanded to an enormous, enormously larger population of people. You know, we're used to when we think about consumers and technology, thinking about people in the United States and countries that are like the United States. Certainly in my work, we see that the franchise, if you will, has been extended to so many other people existing at levels of income that are almost incomprehensible to us. People who make a dollar a day and yet who have become part of this community that are using mobile devices, essentially are on the web in some way. They're on the internet and therefore can be tracked that can have these services provided to them that can strike these same deals. But if I said that I'm disadvantaged in a negotiation with Apple, well then where does the Ethiopian shepherd stand in that Apple's got a million lawyers. I've got one and the shepherd's got none. Who stands for that? I think from my perspective, it's in order to get to real utility out of these types of apps and data, we need to allow for a lot of iterative innovation and exploration of what is the right thing to look at for somebody who's being, whose drugs are being titrated for depression or anxiety or Crohn's or MS. It's not every detail that we now have the ability to measure it. We have to move up to what are these, if you will, sort of behavioral biomarkers and the equivalent of the data that's going to inform the care. And so we somehow have to increase the appetite and the readiness of the health and the medical community to really iterate on generating the evidence they need to bring the best sets of care and services to people. We can do a lot more by giving people utility to manage their diet, their money, their time, and the activities they do, the things they spend consuming. So I think that it's a combination of, there's lots of data that we don't consider health data. So I would add in there our Netflix viewing, right? So Netflix binging. As a proxy for sedentary. For sedentary or sleep disruption, right? There's lots of things in there as that is another source of data. Sleep is increasingly seen as relevant, all kinds of things that are directly health and even in medical. So we need to get access to that data but then it all sounds incredible but to turn it into something meaningful, we have work to do as a community. You know, getting to your point about how with phones it wasn't the phone companies, it was the computer companies. I mean, to some extent what we're seeing is the computerization of everything, right? Because of Moore's law and just the decreasing price, decreasing size of these processors. But I think also, I think this doubles down on your point to some extent. But I mean, I'm not really sure if we look to the medical community, really, when it comes to these things to improve our health. And one of the most exciting things to me, and I could say this as a doctor, is the opening up of this process of looking at our data and looking for solutions that will benefit our health. The opening up of this process to people who are not within the current medical system. I mean, certainly if you look at anyone's health in this room, what percentage of your health is determined by your, I've actually have one of my patients here, but he'll be sorry to learn that I feel that 99% of his health is not determined by the five minutes a year that he spends with me. It's determined by diet, exercise, all these other things. So I think it's a mistake to think that providing this data to the medical community, that's gonna help them solve all these problems. I think the more exciting thing is providing this data to other folks who don't have the same business models or preconceptions of the medical community that may prove to be the most fruitful aspect. But I think that touches and leads into my piece, which is what kind of literacies we're still having to develop now that we have all this data is like, you know, are kind of physicians ready to understand the kind of statistics of the day to day view and kind of extrapolating that to how consumers actually look at your everyday data. Like what does it mean that this week looks like that? How is that gonna impact me on the long term? And so I think there's this larger picture about the apps and services and kind of things that go on top of all the data. Once you have access, that's one thing, but the next thing is to actually make it meaningful access and meaningful, what action should I take next? What kind of prescription comes out of this data that I've gotten access to? And there are two pieces to that. One is actually there is, we shouldn't leave behind the scientific method and when things are evidence and when they really have, when it's correlation and when it's causality and when it's nothing, you know, but some biased recollection, right? So there is, we can do a good job at really understanding what should be actionable, not to respond to every little blip, but it's overall some trend or second derivative or whatever it is. And then there's bringing it and all of the human computer interaction and the design element of making it meaningful and something that somebody will actually act on via clinician or patient. So I mean, you're talking about sort of applying a scientific method and making sure that things are, that you're taking account not just accounting for blips, but at the same time when you talk about on the individual level, well, you're left with basically a series of blips, right? I mean, when an individual is thinking about the data they're thinking about, well, okay, I got these recommendations based upon, I guess what I entered in terms of what I ate, how long my Netflix queue was and this and that and it gives me this recommendation. So I mean, how do you get from, how do you generalize from the specific, how do you actually, how do you apply the general to the specific in that? Is that, are people going to be worried that you're just applying a formula to that? Yeah, I mean, I've been spending a lot of time thinking about like what normal is anymore, like where the kind of curve lies and where anyone has the context of my data versus everyone else and having literacy about that. I think the other side of it is the, how these systems are actually designed and how they're supporting certain behaviors and certain activities. But I give the example of the Fitbit where, that 10,000 step is a good goal for the whole of society but like where does that even come from and there's contestation about like why that is a good metric. But in the context of I recently had hip surgery and I was recovering and like wanting to change my use of the Fitbit as a recovery device and that really not being a kind of piece of the interaction and kind of, so how we can be actually more responsive with how we interact with these systems. Is that a real story? It is, yeah. Because we, you never know on these panel things but we have some research going on with people at hospital for special surgery, HSS in New York which does some of the largest number of hip disease per week of many places in the US I think and they are starting to look at exactly that data as what can this tell us about people's recovery patterns? How can we see when someone's plateaued and then you take that further of it being feedback for somebody's use in physical therapy and so the one thing I want to say is that while ultimately like what is normal and how we understand the bigger aggregate we will get there and we'll learn a lot but right now we're at this point that just looking for in-person changes is where it can come along right away without any more elaborate models and even one patient at a time because you as a clinician know that after hip surgery you're expecting somebody, they're gonna initially be ambulating walking a lot less and you're looking for some what's their maximum cadence or some change in their cadence and then you're looking for some drop off there's already knowledge in the medical system we just have the ability to now measure it 24-7 where we didn't before so we already sort of know what we're looking for one person at a time looking for changes within that person relative to their baseline we get to start there before we even take on the existential question of what normal is. Thank you, great. I think we're about ready to open the floor for questions. Adrian Guapar, patient privacy rights. The one definition of ownership to get back to the original topic is something where you don't have a contract with another counterpart like a privacy policy or the terms of views or the consent and this causes me to ask about open source software for these devices and for these apps. We don't typically use as personal devices things that have secret ingredients pharmaceuticals or whatever surgery in general when you talk about it from the physician point of view. Where will we draw the line in trying to get to all of the things that you're talking about on secrecy in these things that are highly personal and that are either attached to us or attached to this phone that we own? I think it's a great question and I mean as a doc and as a software developer with Magpie I mean I could say that we've sort of looked at this issue but I think there are many things to recommend open source. I think within the commercial software or actually within the software industry in general one doesn't see open source except in a symbiosis with closed source and this has been because it's hard to monetize open source. It's hard to get revenue for that and so I think there may very well be benefits to open source and then the question will be if we decide that open source is better for providing this kind of software for the reasons that you mentioned the question then will be who's gonna pay for it? How do we get that paid for? Again this has been traditionally this has been problematic which is again why you see open source always as kind of a something that's used in conjunction with closed source. You know Apple has open source aspects Google does but Google wouldn't have. Yeah but I mean again the question becomes who pays for it because medicine is of course famously an extremely expensive industry in the United States with extremely high prices and those prices perhaps may be one of the things that has supported that openness within medicine. They don't have to make money off of that aspect but that may not be applicable or desirable when it comes to software. So it's one of several potential barriers is the lack of access to the code or even at least access to an API. For sure. Well I think it gets back to the standards question which is the other piece of open source is possible because standards exist. So yeah that seems like the lever even in proprietary systems to be able to say this is going to work with other things as well. And in a number of areas I mean are we even is that even more ambitious than what we have right now which is merely access to the sort of the black box the input at the output of what's being done. No but I think it's a great question. I mean because the fact is what happens in the black box you can't assume that that is harmless. But I mean you want to know what's going on in there. It's a fascinating question. It's the barriers to entry in things related to pharma are so different from software the ability to define IP and then just protect it that way it's just it's really hard to relate from one to the other. But being able to define Kevin sitting here in front of us being able to define what a piece of software does even if you don't have access to all the code within it is something you can do. We don't require that. It's a little bit what the app store and the play store tried to do on the apps that you then download because they actually study the code and say it accesses your location information it sends this contact information there. And they're trying to do something that is relatively the equivalent and I think pursuing and trying to pursue that further is about as far as we will get without shutting down software innovation. Another question. I don't know for us from the Wilson Center. Sorry for this. Can you hear? I'm from the Wilson Center. I must call out there. So in this mobile health model there has been a lot of emphasis on individual empowerment. And I was wondering if we can see if there was actually a dark side behind that model of empowerment, empowering patients. And one problem or one take on this is that in a way you are transferring a lot of responsibility for self diagnosis and for understanding to an individual to the patient itself or to a citizen. And so to what extent are we actually transferring responsibility and for risk assessment legal responsibility to the individual? In a way you could see this as a way of you have not only corporeal responsibility but also genetic responsibility. So people who are really engaged and literate are going to be extremely, I mean doing good in that system but what about those at the margins? How do we design empowerment for them? And we're talking about data ownership, retribution, solidarity. How do we think about retribution also in that sense? So independence is good but mandatory independence not necessarily what you want a patient to be facing. So yeah, questions? I mean I think again as a doc I think you're right that certainly you'd be shifting some responsibility if the patient is responsible for more what they do. But I would emphasize what I mentioned before which is that you already have responsibility for most of your health, the overwhelming amount of your health.  Not the thing that's responsible for your health unless you're a child or an older person or a pregnant woman in which case it might truly be considered a healthcare system. I think it's also important to remember that the driving force behind what we're seeing of shifting as you point out the responsibility or the actions of maintaining your health to individuals is not in general being promoted by the healthcare system. It's being promoted by the computer industry. And by the technology industry I would say. So I think you're right that that process is happening but I wasn't quite sure if you were suggesting that it was the medical industry that was sort of hoping to get out of that responsibility or transfer responsibility away. I think the medical industry for the most part is completely baffled by or unaware of what's going on in technology right now. So I think these changes are happening but I don't think they're being driven necessarily by the, I don't think they'd be driven at all really by the medical care system. I'm sorry, I think we were out of time for questions but does anyone on the panel have final thoughts that they want to close out with? We've covered a lot. One last thing. Well, then I'd like to thank all of our panelists for all their time and all their insights. Thank you. Thank you so much.


Welcome back to School Car Science with Tim and Jaco and today we have got a follow-along mobility session for you. Tim's going to be doing the rep showing you his amazing mobility and flexibility and you're going to be following along at home so let's get started now. So we're going to put these exercises together in a movement flow that means that one is going to go straight into the next and just going to link them together so we can start to move through a few different positions and start to get nice and mobile and hips without faffing about and changing positions all the time. So we're going to show you first now Tim's going to go through all of the different exercises and then separately and then we'll build them together in follow-alongs so this is just so we know what you're doing and what you're going to go to. So first one is hip shoulder width apart we're going to drop into the bottom of a deep squat position in there you're going to be exploring a little bit side to side what this position is like you're going to work as low or as comfortable range of motion that you've got. From there the next one he's going to do is he's going to take his knee into some internal or his hip into internal I say so taking the hip across the other side of the body. After that he's going to be raising the arms up above the head trying to stay as deep as he can he's going to wrap those out making sure we've got nice controlled movement all the way through any of these and most of all making sure you're working in the range that is comfortable for you. After that he's going to go down to a sumo squat so hands go onto the toes we're going to stretch those hamstrings like in his pike position and then from there we'll walk his hands out to the bottom of a or sorry the top of a push-up position and then it's going to go into like his lizard type position where we're opening the hip up and again exploring ranges on one side back to your press up position exploring ranges on the other. After these done those we're going to walk ourselves back and then we're going to be in that little stretch of the hamstring again before standing working our way up to standing and then the last little couple are some lateral movements so going out to the side again deep as you can back to the middle and then we're going to go all the way across the other into our cut seat where the knee crosses over if you can you're going to sit all the way down onto the floor like Tim and then you're going to stand back up and then we'll just simply do the other side as far as you can back to the middle leg goes then behind knee crosses over down to the floor sit down if you can then stand back up take a deep breath and that's you mobilise and ready for either your session ahead or maybe you're going to go to bed now you've done it before you before you read or to work time or to work this is the type of thing that actually could be done brilliantly at any time or point of the day as well as a standalone session on its own the more reps you do of these the more you're going to get a blow on so we're going to go for five of everything in this so get yourselves ready there's no going to be no breaks at all you're going to follow along let's get into the full flow now right so you've got a drink are you ready let's go so first thing bottom of this deep squat position as deep as you can go we're going to explore a little movement one side one two three four five six seven eight nine ten so that's five on each side then we've got five we're crossing the knee over so one and then one on the other side good then two trying to stay as low as we can at all times but just working where you can hopefully the more we go through these the more you're going to feel that you're starting to get loose and then five that's the last one across good and then we're going to do five raises the key on this one is trying to maintain the hips down so the arms are going to raise up it's going to pause team's got lovely shoulder and hip mobility and range so we're able to go through this slowly control little pause as we go at the top for four and then the last rep five that's working the whole of your body now hands go down onto the floor bum goes up in the air we're stretching the hamstrings just going to pause there for a second before we're going to walk our hands out to this top of the push-up position so then we're going to do five reps on each side it's in a little explore and takes the leg back other side so that's one on each side feet go back a little explore that hip range that's two that's the second rep on this side here comes number three pushing that hip out you want to actively push the knee out with those glutes there's number four and again trying to get as we go through these reps you're going to feel like perhaps you can drop a little bit lower and a little bit deeper into those positions and then this is the last rep five good back to the press position and you're going to start to walk your hands back up your shoulders you're working hard in there little pause the bottom to stretch those hamstrings again before rolling yourself back up to the top then we're going to do five reps on each side and each rep has almost got two in remember so steps outside as deep as you can trying to stay nice and upright as much as you can leg comes all the way around the back knees cross cross over can sit all the way down to the floor and there's the first rep come out to the other side back through making sure we're challenging our balance a little bit here and we're on one leg as well so there's one rep on each side so into rep number two comes back a little bit of balance cross the leg over knee comes down bum all the way to the floor this time back up second rep on the left side control all the way through working as deep as you can so you can go all the way down to four there's number two so we've got three left to go he's working a little bit now we've we've glossed this up as an ability and flexibility but now the glutes are starting to work really strong in this position the deeper you drop down there the more the glutes are going to work and then the same as you drive back up here that right foot is working hard as you drive through so here comes rep number four cross over drive back up the left side rep number four brush back through a little bit of balance good last one come to the left done so there we have a nice little lower body mobility routine i definitely feel loosey goosey right now and you probably worked a little bit harder than you thought you'd dress that up as a little bit mobility but one of the most important things about creating range of motion is not just going into into new shapes and new unforeseen lands of range in those hips and those shoulders but actually being strong there so hopefully your glutes will feel like they've been working hard and you've got a little bit of a sweat on as well as for you and as tim said loosey goosey one of the most important things in mobility is we keep using range of movement so that's literally going to take you a few minutes to go through you're going to maintain some good range of movement and improve it keep your body moving well probably the biggest investment you can make just to move well and that is going to set you up really fine even possible whatever that might look like yeah there's no reason why you couldn't go through that um have a little rest then go through that sequence again or increase the number of reps you might spend a bit more time in some of those positions or you might rather than doing five of each you might do eight nine ten uh and and so on and just work with where your body's at and what feels good for you is a good cue and key to improving that range of motion and making sure you have a little bit of fun with it as well so go have a play and we'll see you next time so until next follow-along workout class dismissed