1 00:00:05,320 --> 00:00:10,559 Speaker 1: Welcome to Prognosis. I'm Laura Carlson. It's day one five 2 00:00:10,800 --> 00:00:15,680 Speaker 1: since coronavirus was declared a global pandemic. Our main story. 3 00:00:16,680 --> 00:00:19,440 Speaker 1: Your fitbit can tell you a lot about how your 4 00:00:19,440 --> 00:00:24,000 Speaker 1: body is working. Now. Scientists are wondering if wearable technology 5 00:00:24,079 --> 00:00:27,560 Speaker 1: like this can help detect the earliest signs of coronavirus 6 00:00:27,640 --> 00:00:33,600 Speaker 1: infection and help us combat the pandemic. But first, here's 7 00:00:33,640 --> 00:00:47,040 Speaker 1: what happened in virus news today. The outlook for the 8 00:00:47,040 --> 00:00:50,880 Speaker 1: global economy in the wake of coronavirus just got worse. 9 00:00:51,880 --> 00:00:56,040 Speaker 1: The International Monetary Fund said they now project the recession 10 00:00:56,080 --> 00:01:00,120 Speaker 1: will be deeper and the recovery slower than they thought 11 00:01:00,000 --> 00:01:04,480 Speaker 1: it would two months ago. Today, the i m F 12 00:01:04,600 --> 00:01:08,520 Speaker 1: said it expects global gross domestic product to shrink four 13 00:01:08,640 --> 00:01:14,000 Speaker 1: point nine percent this year. They had predicted three percent 14 00:01:14,240 --> 00:01:18,240 Speaker 1: in April. The shock to the supply chain was larger 15 00:01:18,280 --> 00:01:22,080 Speaker 1: than the i m F anticipated, and for nations struggling 16 00:01:22,120 --> 00:01:26,080 Speaker 1: to control the virus spread, a longer lockdown also will 17 00:01:26,120 --> 00:01:30,200 Speaker 1: take a toll on growth That accounts for the fund's 18 00:01:30,440 --> 00:01:36,360 Speaker 1: more pessimistic view. In the US, spikes in sun belt 19 00:01:36,400 --> 00:01:41,280 Speaker 1: states continue while the virus situation improves. In former hotspots. 20 00:01:42,319 --> 00:01:46,440 Speaker 1: Now New York, New Jersey, and Connecticut will require visitors 21 00:01:46,560 --> 00:01:50,960 Speaker 1: from virus hotspots to quarantine for fourteen days to avoid 22 00:01:51,000 --> 00:01:55,840 Speaker 1: a resurgence in cases. The announcement is a reversal from March, 23 00:01:56,240 --> 00:02:00,320 Speaker 1: when Texas and Florida ordered quarantines from the Northeast dates 24 00:02:00,440 --> 00:02:06,400 Speaker 1: where cases were surging. Arizona, California, and Texas all set 25 00:02:06,480 --> 00:02:13,120 Speaker 1: records for new cases on Tuesday. Finally, another disease could 26 00:02:13,200 --> 00:02:19,440 Speaker 1: ravage certain populations because of the COVID nineteen pandemic. Tuberculosis 27 00:02:19,480 --> 00:02:25,639 Speaker 1: could cause at least one ten thousand additional deaths in China, India, 28 00:02:25,800 --> 00:02:29,040 Speaker 1: and South Africa, according to a study published by the 29 00:02:29,080 --> 00:02:34,239 Speaker 1: European Respiratory Journal. Disruptions to health services and delays to 30 00:02:34,360 --> 00:02:39,679 Speaker 1: diagnosis and treatment will likely increase TV fatalities. That could 31 00:02:39,720 --> 00:02:43,440 Speaker 1: have a greater impact on drug resistant TB patients as 32 00:02:43,440 --> 00:02:57,000 Speaker 1: they often require longer treatment. And now for today's main story, 33 00:02:58,480 --> 00:03:01,160 Speaker 1: the NBA is getting play. There's the option to wear 34 00:03:01,160 --> 00:03:05,079 Speaker 1: a device that tracks their health data when basketball games 35 00:03:05,120 --> 00:03:09,880 Speaker 1: begin this July. This device, called an aura ring, can 36 00:03:09,919 --> 00:03:13,560 Speaker 1: measure things like the body's temperature and heart rate. The 37 00:03:13,600 --> 00:03:15,640 Speaker 1: hope is that it could provide the leak with early 38 00:03:15,720 --> 00:03:19,560 Speaker 1: warning signs that someone may have contracted an illness like 39 00:03:19,800 --> 00:03:25,240 Speaker 1: COVID nineteen. Bloomberg reporter Kristen V. Brown reports that the 40 00:03:25,280 --> 00:03:28,400 Speaker 1: move is part of a larger conversation about whether or 41 00:03:28,440 --> 00:03:31,840 Speaker 1: not wearable technology like a fitbit or an Apple Watch 42 00:03:32,120 --> 00:03:37,480 Speaker 1: can help fight the pandemic. Here's Kristen. Every day I 43 00:03:37,480 --> 00:03:40,400 Speaker 1: get a text from a Stanford research group reminding me 44 00:03:40,480 --> 00:03:43,200 Speaker 1: to fill out a series of questions. The questions are 45 00:03:43,200 --> 00:03:48,040 Speaker 1: pretty straightforward. They're mostly related to COVID nineteen. Do you 46 00:03:48,040 --> 00:03:53,920 Speaker 1: have any symptoms to report today? No? Feeling good? Have 47 00:03:54,040 --> 00:03:58,200 Speaker 1: you received the results from any COVID nineteen tests today? Nope? 48 00:03:59,040 --> 00:04:05,080 Speaker 1: Any other stems? I guess not really kind of had it? 49 00:04:05,120 --> 00:04:08,520 Speaker 1: Tell me do you think that counts. I've been participating 50 00:04:08,560 --> 00:04:11,240 Speaker 1: in this study for several weeks now, and when I'm 51 00:04:11,240 --> 00:04:14,080 Speaker 1: done with my questionnaire, I also send them the data 52 00:04:14,160 --> 00:04:20,040 Speaker 1: my Apple Watch has captured for the day. The study 53 00:04:20,200 --> 00:04:23,160 Speaker 1: is just one of several happening around the world a 54 00:04:23,279 --> 00:04:26,400 Speaker 1: scientists raised to find out if wearable technology can play 55 00:04:26,400 --> 00:04:29,600 Speaker 1: a role in the fight against the pandemic. They want 56 00:04:29,640 --> 00:04:32,159 Speaker 1: to see if our fitbits can help predict whether users 57 00:04:32,200 --> 00:04:36,440 Speaker 1: have contracted COVID nineteen days before they exhibit any discernible 58 00:04:36,440 --> 00:04:39,920 Speaker 1: symptoms like a fever. A lot of the time, when 59 00:04:40,000 --> 00:04:44,039 Speaker 1: people talk about predicting trends and infectious diseases like COVID nineteen, 60 00:04:44,720 --> 00:04:48,440 Speaker 1: they compare it to predicting the weather. Neither is a 61 00:04:48,480 --> 00:04:52,599 Speaker 1: sure science. You can just make an educated guess. But 62 00:04:52,680 --> 00:04:55,600 Speaker 1: to help more accurately predict the weather, we have all 63 00:04:55,680 --> 00:04:59,600 Speaker 1: kinds of sensors in place all over the world. Wearables 64 00:05:00,080 --> 00:05:12,000 Speaker 1: function in the same way for disease prediction. Initially, the 65 00:05:12,080 --> 00:05:15,000 Speaker 1: makers of devices like the fitbit how did the ability 66 00:05:15,000 --> 00:05:18,320 Speaker 1: of wearables to help users count steps, stay active, or 67 00:05:18,360 --> 00:05:22,320 Speaker 1: monitor sleep. Increasingly, though they have also been used to 68 00:05:22,440 --> 00:05:26,920 Speaker 1: detect illness. Past research has shown that this biometric data 69 00:05:27,240 --> 00:05:31,279 Speaker 1: could support health problems, including high blood pressure, heart arrhythmia 70 00:05:31,279 --> 00:05:35,200 Speaker 1: as an early stage cancer. If wearables could accurately detect 71 00:05:35,240 --> 00:05:38,880 Speaker 1: COVID nineteen cases early on, it could aid efforts to 72 00:05:38,880 --> 00:05:42,839 Speaker 1: help monitor new outbreaks of the virus. This could take 73 00:05:42,880 --> 00:05:46,240 Speaker 1: some of the pressure off of testing and contact tracing programs. 74 00:05:47,160 --> 00:05:50,159 Speaker 1: Jennifer Ratten is leading one of these studies at SCRIPTS 75 00:05:50,200 --> 00:05:54,960 Speaker 1: Translational Research Insto in San Diego. California. She says they're 76 00:05:54,960 --> 00:05:58,159 Speaker 1: basing these studies off of previous research that was published 77 00:05:58,200 --> 00:06:02,000 Speaker 1: this January. But we found is that we had a 78 00:06:02,080 --> 00:06:05,200 Speaker 1: data set of two hundred thousand Fitbit users who were 79 00:06:05,240 --> 00:06:07,719 Speaker 1: their device for about two years, and we found that 80 00:06:07,800 --> 00:06:12,599 Speaker 1: if you identified weeks where individuals had arresting heart rate 81 00:06:12,680 --> 00:06:16,240 Speaker 1: and sleep that was greater than their individual norm or 82 00:06:16,279 --> 00:06:20,560 Speaker 1: average during the study period, that the proportion of Fitbit 83 00:06:20,680 --> 00:06:24,479 Speaker 1: users each week who had this abnormal data was predictive 84 00:06:24,720 --> 00:06:29,400 Speaker 1: of influenza like illness, and we were able to predict 85 00:06:29,520 --> 00:06:33,680 Speaker 1: influenza like illness in real time. Jennifer says that this 86 00:06:33,760 --> 00:06:36,919 Speaker 1: kind of data could be really powerful when responding to 87 00:06:36,960 --> 00:06:40,719 Speaker 1: COVID nineteen Scripts is monitoring the heart rate of about 88 00:06:40,760 --> 00:06:45,000 Speaker 1: thirty volunteers to look for early signs of disease. So 89 00:06:45,080 --> 00:06:48,680 Speaker 1: getting this data in real time has the potential to 90 00:06:48,760 --> 00:06:53,120 Speaker 1: really improve outbreak response and to be able to identify 91 00:06:53,360 --> 00:06:56,039 Speaker 1: when things are occurring and also be able to zoom 92 00:06:56,040 --> 00:06:58,840 Speaker 1: in and identify kind of where those hot spots are. 93 00:06:59,560 --> 00:07:02,320 Speaker 1: So in U s um one in five Americans where's 94 00:07:02,320 --> 00:07:05,960 Speaker 1: a smart watch or fitness tracker, So there's the potential 95 00:07:06,040 --> 00:07:09,800 Speaker 1: to really harness a large amount of data for many 96 00:07:09,960 --> 00:07:21,720 Speaker 1: users across the country. Like a lot of other research groups, 97 00:07:21,880 --> 00:07:24,680 Speaker 1: Jennifer's work focuses on the heart rate data that these 98 00:07:24,720 --> 00:07:28,240 Speaker 1: devices collect. Heart rate, it turns out, can be a 99 00:07:28,320 --> 00:07:32,480 Speaker 1: really good predictor of whether someone is getting sick. Jennifer 100 00:07:32,520 --> 00:07:35,320 Speaker 1: says heart rate data can actually be a far better 101 00:07:35,400 --> 00:07:38,960 Speaker 1: predictor of illness than more noticeable symptoms like a fever. 102 00:07:40,000 --> 00:07:43,559 Speaker 1: That's especially true for COVID nineteen since so many people 103 00:07:43,640 --> 00:07:47,480 Speaker 1: are asymptomatic, and often it seems there are changes to 104 00:07:47,520 --> 00:07:50,400 Speaker 1: a person's heart rate long before other symptoms of an 105 00:07:50,440 --> 00:07:55,280 Speaker 1: illness appear. Similar research from Stanford showed that wearables were 106 00:07:55,280 --> 00:07:58,800 Speaker 1: able to detect an infection as early as nine days 107 00:07:58,920 --> 00:08:03,559 Speaker 1: before someone started showing symptoms of COVID nineteen. Jennifer also 108 00:08:03,600 --> 00:08:07,120 Speaker 1: says that because many people are asymptomatic, trying to use 109 00:08:07,200 --> 00:08:11,080 Speaker 1: data like temperature can miss a lot of cases. So 110 00:08:11,840 --> 00:08:15,200 Speaker 1: lots of people with COVID don't um have a fever, 111 00:08:15,440 --> 00:08:18,960 Speaker 1: they don't develop one early on in their infection, and 112 00:08:19,000 --> 00:08:22,840 Speaker 1: there's also many asymptomatic cases out there who don't develop 113 00:08:22,880 --> 00:08:27,360 Speaker 1: any symptoms. So we think that just looking at temperature 114 00:08:27,360 --> 00:08:30,840 Speaker 1: alone you might miss many cases out there, but we 115 00:08:30,960 --> 00:08:34,920 Speaker 1: think that rusting heart rate and these other metrics collected 116 00:08:34,960 --> 00:08:40,120 Speaker 1: with your wearables can potentially be an earlier warning signal 117 00:08:40,240 --> 00:08:44,320 Speaker 1: that something's going on um and the Again, the great 118 00:08:44,360 --> 00:08:47,319 Speaker 1: thing about the wearables is that we get each person's 119 00:08:47,400 --> 00:08:51,480 Speaker 1: unique individual baseline, so that we're not comparing you to 120 00:08:51,760 --> 00:08:55,720 Speaker 1: the population average. We're comparing you to yourself over time, 121 00:08:56,240 --> 00:08:59,760 Speaker 1: and that allows us to kind of identify more subtle 122 00:08:59,800 --> 00:09:03,240 Speaker 1: changes in your data that may indicate something's going on 123 00:09:03,320 --> 00:09:06,360 Speaker 1: in your health. This data could not only predict who's 124 00:09:06,400 --> 00:09:10,880 Speaker 1: getting sick, but monitor a huge number of people relatively easily. 125 00:09:11,600 --> 00:09:15,080 Speaker 1: Kimnall is the director of Telehealth at stony Brook University 126 00:09:15,240 --> 00:09:18,200 Speaker 1: on Long Island in New York. She has her own 127 00:09:18,200 --> 00:09:22,480 Speaker 1: wearable study. She says that this information could potentially be 128 00:09:22,559 --> 00:09:27,120 Speaker 1: really valuable in helping states to reopen safely. The hope 129 00:09:27,520 --> 00:09:32,400 Speaker 1: is that we as a society define ways to determine 130 00:09:32,520 --> 00:09:36,160 Speaker 1: risk for COVID, and you know, whoever determines that wins 131 00:09:37,480 --> 00:09:42,520 Speaker 1: the grand prize of helping us reopen safely. The question 132 00:09:42,520 --> 00:09:44,760 Speaker 1: where really is asking is what in the role of 133 00:09:44,800 --> 00:09:48,680 Speaker 1: technology and wearable tech in in contributing to that To 134 00:09:48,760 --> 00:09:51,760 Speaker 1: answering that question of who's at high risk and who's 135 00:09:51,760 --> 00:09:54,439 Speaker 1: going to get sick and we don't know that yet, 136 00:09:54,800 --> 00:09:58,800 Speaker 1: you know. And so there's promise that if we have 137 00:09:58,960 --> 00:10:02,920 Speaker 1: something that's passed of enough that gives us early flat 138 00:10:02,960 --> 00:10:08,240 Speaker 1: red flags, that we can then act upon that data. 139 00:10:08,440 --> 00:10:11,360 Speaker 1: Kim had COVID nineteen herself, and that was a big 140 00:10:11,400 --> 00:10:15,200 Speaker 1: part of what motivated her work. I was very committed 141 00:10:15,280 --> 00:10:17,600 Speaker 1: to understanding my own risk. You know, what was my 142 00:10:17,679 --> 00:10:19,920 Speaker 1: temperature going to be and will I get sick again? 143 00:10:20,480 --> 00:10:24,120 Speaker 1: These are these are the premise um questions, hypotheses that 144 00:10:24,160 --> 00:10:27,720 Speaker 1: we have in the study. She says. The data isn't 145 00:10:27,760 --> 00:10:31,120 Speaker 1: just important for public health officials, it could also help 146 00:10:31,160 --> 00:10:35,479 Speaker 1: people make better decisions in their own daily lives. Devices 147 00:10:35,559 --> 00:10:38,920 Speaker 1: that can reliably predict the onset of COVID nineteen could 148 00:10:38,960 --> 00:10:43,760 Speaker 1: play a major role in reopening workplaces, restaurants, and stores safely. 149 00:10:44,200 --> 00:10:47,800 Speaker 1: A company could, for example, encourage returning workers to use 150 00:10:47,800 --> 00:10:50,480 Speaker 1: an Apple Watch to look for signs there in early 151 00:10:50,559 --> 00:10:53,720 Speaker 1: stages of the illness. The NBA is planning to do 152 00:10:53,840 --> 00:10:57,719 Speaker 1: just that. As basketball games resume in July. You can 153 00:10:57,760 --> 00:11:00,400 Speaker 1: say like, well, my temperature is rising and have a 154 00:11:00,440 --> 00:11:03,559 Speaker 1: fever yet, But my ring tells me that I might 155 00:11:03,800 --> 00:11:06,480 Speaker 1: basick of COVID, let me sell me, let me social distance. 156 00:11:06,520 --> 00:11:09,840 Speaker 1: That would be the dream of what we could aspire 157 00:11:10,000 --> 00:11:14,319 Speaker 1: towards if we had the ability to know for certain 158 00:11:15,400 --> 00:11:18,960 Speaker 1: we could rely on that data On an individual level. 159 00:11:19,120 --> 00:11:22,720 Speaker 1: Of course, that data cannot definitively tell you whether you're 160 00:11:22,760 --> 00:11:26,160 Speaker 1: coming down with COVID nineteen or the flu, or maybe 161 00:11:26,200 --> 00:11:29,600 Speaker 1: just experiencing an elevated heart rate because you're excited about 162 00:11:29,600 --> 00:11:33,680 Speaker 1: a first date. But when taken together, all of that 163 00:11:33,800 --> 00:11:46,760 Speaker 1: data suddenly becomes meaningful. That was Kristin V. Brown. You 164 00:11:46,800 --> 00:11:50,560 Speaker 1: can read her story with Tom Giles Unwearables in the 165 00:11:50,640 --> 00:11:55,440 Speaker 1: June two issue of Bloomberg Business Week or at Bloomberg 166 00:11:55,520 --> 00:11:58,959 Speaker 1: dot com. And that's our show Today. For coverage of 167 00:11:59,000 --> 00:12:01,760 Speaker 1: the outbreak from one hut and twenty bureaus around the world, 168 00:12:02,160 --> 00:12:06,640 Speaker 1: visit Bloomberg dot com slash Coronavirus and if you like 169 00:12:06,760 --> 00:12:09,720 Speaker 1: the show, please leave us a review and a rating 170 00:12:09,960 --> 00:12:13,360 Speaker 1: on Apple Podcasts or Spotify. It's the best way to 171 00:12:13,400 --> 00:12:18,240 Speaker 1: help more listeners find our global reporting. The Prognosis Daily 172 00:12:18,400 --> 00:12:22,680 Speaker 1: edition is produced by topor Foreheads, Jordan Gas Pure Magnus 173 00:12:22,720 --> 00:12:27,600 Speaker 1: Hendrickson and me Laura Carlson. Today's main story was reported 174 00:12:27,600 --> 00:12:32,760 Speaker 1: by Kristin V. Brown. Original music by Leo Sidrin. Our 175 00:12:32,880 --> 00:12:37,360 Speaker 1: editors are Rick Shine and Francesca Levy. Francesca Levy is 176 00:12:37,400 --> 00:12:40,920 Speaker 1: Bloomberg's head of podcasts. Thanks for listening.