Christina Reynolds, PhD
Christina Reynolds received her Ph.D. in astrophysics from University College London and a Master's degree in software engineering from Harvard University. She has been a data scientist with ORCATECH, focusing on developing algorithms to analyze ORCATECH's large and diverse data set.
Much of her research career has involved developing software algorithms to fabricate and test the optics for the European Extremely Large Telescope and the IRIS space telescope. At ORCATECH, she focused on designing a wide variety of algorithms for deriving information about life and health patterns from ORCATECH’s sensor data, including characterizing activity and sleep behaviors.
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Explore our Wearables, Experience sampling method (ESM), Sleep, Heart rate variability (HRV), Sedentary Behavior, and Physical Activity article collections for insights on related articles.
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Refer to our article "Physical Activity and Sedentary Behavior Measurements" for an exploration of active and sedentary lifestyle assessment methods.
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Learn about actigraphy in our guide: Exploring Actigraphy in Scientific Research: A Comprehensive Guide.
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Gain foundational ESM insights with "Introduction to Experience Sampling Method (ESM)" for a comprehensive overview.
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Explore accelerometer use in health research with our article "Measuring Physical Activity and Sedentary Behavior with Accelerometers ".
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For an introduction to the fundamental aspects of HRV, consider revisiting our Ultimate Guide to Heart Rate Variability.
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This is the Physical Activity Researcher Podcast, a podcast for researchers of sedentary behavior, physical activity, and sports. Join for a relaxed dialogue about research design, practicalities, and well, anything related to research. Learn from your fellow researchers useful and relevant information that does not fit into formal content and limited space of scientific publications. And here is your host, researcher and entrepreneur, Ollie Tickenit. Yeah, sounds really interesting. I'm looking at my notes, and they are so good messes. I've been writing a lot of things. There's so much interesting points here. They have some interesting points you still want to add in this inter-daily stability, inter-daily variability. I think it's an interesting metric, looking at people's health that's fairly easy to calculate, using data that's fairly easy to collect. So, if you go ahead and just say that, alright, what we're looking at is active versus rest, and we let go of the idea that we can absolutely measure sleep, you can still get interesting information from it, and it's data that you can get from an actograph. I mean, so many people wear devices that can measure their activities, so that's data that already exists for people. There are bed mats that you can put under the mattress to measure when people are asleep, but it's data that's fairly easy to collect over long periods of time without really asking a lot of the person you're collecting the data from, because it's unobtrusive. So, I think it's an interesting metric. It should be kind of standardized in the data that we're collecting about people's activity cycles. Yeah, that's very interesting, and we actually got a grant to measure. We are measuring with the Taiwan accelerometer that can measure when the person is sitting, standing, walking, cycling and different activities, and then we will be using bed sensor to measure their sleep quite nicely. Do you have some ideas? How should we analyze the data that we get pretty good data for probably three months from persons that we get the sleep, we get the activity and different levels of resting and being active with your astrophysics background and looking at the different things, surface and so on. Do you have some ideas? What could be interesting to look from this data? I think when working with a large data set like that is, I think it's really useful to pause for a moment and ask yourself what questions am I asking of this data? And I am completely guilty of this. It's very easy to take a large and interesting data set and go down a rabbit hole of just coming up with different analyses that you can do just because you can do them. But I think in terms of really pushing your science forward and keeping it focused, it's not a bad idea to stop and ask yourself, first, what questions do I want to ask of this study? And second, make sure that your data can actually answer those questions, sit and think about, well, what are the shortcomings of my data? So if you have a group of people you're following for three months, there are definitely one thing that I would fold into that data is looking at weather data and looking at the length of the day, because that's easily obtainable information. And just you could just see how is that affecting people? How is the number of sunlight hours that they could potentially be exposed to? How is that affecting things? So that's one data source that would be easy to take a look at. And also things like looking for patterns. So one thing I looked at in our data sets were what is the effect of a bad night of sleep? So with your data set, you can tell if somebody's having a night where you can tell they're up and wandering around and they're not in bed, how long does it take them to get back into a good sleep cycle after that? So things like that is looking at looking for patterns at different scales. So you could look at patterns over weeks, over months. How do the change of the seasons affect people's sleep and activity patterns? You can also look short, short term. What's the effect of a bad night of sleep? What's the effect of a good night of sleep? You can look for those relationships. Sounds really interesting and we actually have another project which is people working in a super stressful work and they are working three days in a row, 12 hours shifts. And then they have four days of rest and then again three days and they work from seven in the morning until seven in the evening. And yeah, they have problems with physical health and mental health. How do you see these findings that you have found from the variation that it's a shift work. You work three days very hard and then you rest four days. Do you have any ideas how they could improve their health in this kind of setup? One thing that might be interesting to look at would be that recovery time, looking at the inter-daily stability, inter-daily variability. So during their shift work, that's going to be very regimented because they have an external schedule imposed upon them. So their inter-daily stability is going to look very much the same. Their days are going to look the same. They might have more fragmentation and see what their nights look like. And then it would be interesting to see how long does it take them to recover. I imagine that they'll have these three days of intense work. They'll probably have one day of rest. And then how long does it take? How long is that disruption? Because one thing that could happen is you have your intense work, a day of rest. The next day, perhaps you're more sleepless because your sleep schedule has been disrupted and then you have ripple effects from that. Because it might tell you how best to treat that fourth day. What is the best sleep schedule to have for yourself on that fourth day to recover as quickly as possible from the three days of intense work? Do you sleep as much as possible? Do you try and maintain the same sleep hours? What's the best strategy there? Would you visualize it with the caution in a way that you could show that these are the working days and then it goes different than how would you visualize it to really show that what's going on? So what I would do is for each of your seven days, I would calculate both of these metrics, intradayly stability, intradayly variability. They both produce a single number. So each 24-hour period, a single number. And I would graph that so that if you have three months worth of data, that's going to be a very nice graph. You should be able to see trends. And so if there is, you should be able, those three days where they have this very strict schedule on them, those should really stick out. They're going to have a very high intradayly stability because they really look like each other. Well, then what happens? And then the fragmentation, what happens there? So that's what I would do is I would calculate a single metric for every one of those for each day, each 24-hour period, and then plot it over time. We should maybe call it Arabia on the project. And maybe one more question in this way that I think many of our listeners are doing research on sedentary behavior sitting and then activity. And now they are more interested in sleep because it is 24-hour cycle that our days are. If you sleep more, it takes away from sedentary behavior or activity and vice versa. So do you have any ideas, different ways of analyzing this, that how do you divide the day in 24-hour parts? What could be the interesting variables that you can get out? What are the important metrics that could relate to our health or physiology? And these are the things I know people are looking at, but it would be interesting to look at the relationship between when and the quality of sleep, how much sleep the person gets, the fragmentation in their sleep. And you can compare that against both how much activity and when that activity happens. We've all seen the advice, you know, don't do intense exercise before bed, that sort of thing. Well, how true is that? How much effect does that actually have? So you could quantify that and that would work into the circadian cycle research. How much effect not only does how much activity you have on your sleep patterns, but when is that activity? That might be interesting to look at. No, really good points. And I think we have discussed a little bit over one hour. So maybe it's good to start to wrap up. If we go back to your background with astrophysics, interest in music, what other sciences you would see that are beneficial to combine like interdisciplinary work, that what kind of scientists would come to the research group of something else? How do you see this that we would really get new ideas, usually the innovations come from mixing of different backgrounds? What would be the good mix? I think it's always good to have a mathematician. Again, I just did a bachelor's degree in math, but I think the ideas mathematicians come up with are amazing. And like you said, just a wide variety. The other knowledge that would be great to have in this kind of research is people who study human behavior or sociologists, even historians, talking about how people's life patterns have either changed or stayed the same over time. There's a lot of knowledge there that perhaps our field could take advantage of if there was more communication. Yeah, when I was doing my PhD, we had one article collaboration with the mathematicians and it was very interesting. It was super nice. There was a researcher and she was so excited when we were analyzing the data in a new way. Oh, I wish I would be so excited. Analyzing data, so critics or salami. It was super, super nice. Yeah, this has been a very interesting episode. I think I enjoy the most when we have persons like you that are combining different fields. It's really refreshing to discuss. Do you want to advertise anything? Are you looking for collaborators? Are you looking for some kind of data set that you'd like to analyze? Feel free to advertise anything. So we are my group that I work with is the the Sharp Lab at the Portland VA Hospital in Portland, Oregon. We are associated with Oregon Health and Science University and we're led by Dr. Brenda Lim. And we are always interested in collaborating with people about all aspects of sleep and how it affects life and health and people's happiness. Yeah, sounds good. So thank you, Christina. This was really interesting discussion. Thank you for having me. Thanks for joining us this week on physical activity research through podcast. If you like the show, make sure you never miss an episode by subscribing or following the show on Twitter. This podcast is made possible by listeners like you. Thank you for your support. If you found value in the show, we would really appreciate rating on Apple Podcast or whichever app you use. Or if you would, in a real old-school way, simply tell a friend about the show, it would be a great help for us. We have a fantastic lineup of guests for forthcoming episodes, so be sure to tune in. Thank you all for your support and have a great day.
