Welcome to Shannon and Alan’s OER on the Quantified Self.
We share our findings on the quantified self and its application to education, explore some of its benefits, obstacles, and the data collection process, and consider what the future may entail for quantified learners.
Please explore and engage with our OER by considering the reflection questions included throughout the OER. Share your response(s) to any of the questions back on this post.
Enjoy and happy learning!
https://sites.google.com/view/quantifiedself/quantified-self
Shannon & Alan
Hi Shannon,
Thanks for the resources!
I will try those out.
Rich
Hi Shannon and Alan,
Great job on your OER! I quite enjoyed the organization of your site, and I think you were very thorough in examining the topic. It’s one that I have great interest in, and your descriptions and examples shone a bit more of a light on just how pervasive the quantified self is, especially in education.
I have chosen to answer your question “How could you integrate quantified self data into a learning activity? Do the data and tools already exist?”
I teach in a second language, so my mind was immediately drawn to that. I already use “out of the box” tools that use various tools to gather data about student performance, skill development, and time management. The most obvious one is Duolingo, and there are others embedded in sites like Lingua, Prodigy, Minecraft EDU, etc. Your OER prompted me to think how I could create my own method to integrate quantified self data into a lesson, in order to satisfy several of the concerns you mentioned in your pages on Benefits and Obstacles.
Goal:
Improve vocab and language retention in French, in a class of Grade 6 learners
Part A: Quantified Self Data, and Tools used to track:
1. Vocab words learned: Track number of new words learned each week, using a flashcard tool like Quizlet or Anki, or following vocab lessons embedded in another tool such as Duolingo
2. Practice time: Measure time spent practicing with the goal of learning and retention, by using a time-tracking app like RescueTime, Toggle, or even a physical journal or a timer managed by the student. Time spent can also be measured using tools built into language learning apps.
3. Engagement levels: Note language of engagement through observations, or through physical or digital self-assessment tools. This level of engagement could also be measured by using a visual chart, through discussions, or through monitoring/tracking software.
Part B: Analyze, Reflect, and Adjust
Conduct periodic (for example weekly) reviews that engage students individually and as a class. Identify patterns and help them craft personalized goals, and adjust the use of tracking tools or learning methods if they are deemed to be insufficient or inappropriate to the task. This could turn into a feedback loop that could continue throughout future lessons, or be modified for other subjects/tasks.
Part C: Share data, set goals, and stay motivated
Work with students to create visualizations of their progress and their milestones reached, to show how this quantified self data can be helpful. Set goals and challenges where appropriate, and consult with colleagues, families and the students themselves to determine possible next steps.
All of this would have to be done within the guidelines and regulations of your school/district, with an eye to privacy concerns and principles of UDL to make sure that the tools and methods were accessible and fair to all.
Thanks for the prompt to think this through, and for providing the tools for me to explore it in detail.
Sam P
Hi Sam,
Awesome! I envision your students feeling more ‘invested’ in their own learning as they reflect on and gain insights from their own data.
If you need additional ideas for more quantified self data, other data could include phrases learned and practice time could be categorized between time using an app / time speaking to friends / family and time listening or watching to entertainment in the language being learned.
Gamification elements such as rewards for meeting targets could easily be added as well, although you would have to make sure the environment stays competitive in a fun way and doesn’t turn too competitive that it becomes demotivating for students.
Is there every any collaboration with other classes (e.g. math)? The data collected in your class seems like it could integrate well into other subjects as well and it would be neat for students to have this ‘continuity’ between classes.
Hi Alan and Shannon,
Thank you for your very informative OER. I am going to reflect on the educational data section of the website.
When I was taking my Ontario Catholic Leadership Part 2 course through my board, we discussed data extensively and its impact on our teaching. Data encompasses much more than just grades, and I did not fully appreciate its significance until I completed that course this past year. We consistently collect data through jot notes or actual numbers, which is why I felt the need to include it in my A3. For example, in leadership, data is essential for making informed decisions. Simple aspects such as class enrolment or money distribution are all based on data.
Regarding the quantified self, as a teacher, I look at my students’ grades and often internalize them, even though they are not directly attributed to me because my class average goes down. It is disheartening to feel responsible if students fail a class, leading to the belief that I am not good at my job. As you mentioned in your presentation and through others’ comments, quantified data placed upon ourselves can have a lasting negative effect. The question is, how do we prevent that from happening? As educators, how do we prevent our class scores to impact us?
Jasmine
Hello Jasmine,
Thank you for engaging with our A2 OER! I would be happy to chime in on some of your insights being a longstanding educator myself. Class enrolment data and socioeconomic data are some key indicators to the health of our school ecosystems, and often mimics that of the community and city itself. I find that the more that I dive into the teaching profession, the more I engage with the community data such as fertility rate of a city, immigration policies, the local and federal changes that impact important decision making in the health of a school.
I definitely empathize with you on the internationalization of student grades as a reflection of me as an educator. I do think though, that the summative assessments are still a critical piece and valid tool, that does inform us of key skills that students still need to be able to succeed in our society. It should though, be a piece of the holistic picture. I find that this sentiment is best echoed in Cathy O’Neil’s introduction section of her book, Weapons of Math Destruction.
For me, I have begun to shift towards a growing desire of finding out my long term impact on my students, whether or not my philosophy of championing collaboration result in significant long term academic gains beyond my classroom. To help prevent unhealthy internalization, I often have to reflect on my performance by looking beyond any narrow focused, specific academic scores or evaluations that may suggest I am not teaching my students effectively enough, that I look at multiple criteria such as student participation, peer teacher feedback, parent feedback, or any other (either qualitative or quantitative) feedback rather than solely on summative assessment scores that schools often rely on for data tracking purposes. Like all data, the meaning comes from the context, and how we interpret that data is key to ensuring we enable ourselves to continue to work and become better educators.
Hi Shannon and Alan,
Thanks for your work on the OER. Most of the general discussion on data collection and usage recently focuses on actions by corporations and governments to gain insights into individuals; its refreshing to see the the aspects of data used for and by the individual in question.
I think the ideas you bring up fit well with the more finely grained aspects of assessment that we’re seeing in education: instead of simply a percentage / letter grade summary, the use of rubrics to give more detailed feedback is more common. The health apps parallel this as well, not just using a single figure like weight but incorporating heart rate, blood pressure, sleep duration, etc. I’ve also seen some interesting AI demos on taking texts and analyzing them in terms of tone and style, tools which could help give more detailed suggestions on writing styles. For example, pointing out inconsistencies in tone, repetition of words or sentence structure, etc.
Personally, I think detailed analytics based on physiology are a ways away in an education context, except as a support for specific learning challenges. The details provided currently seem too minimal compared to the costs (time and money). The OER I worked on for the 522 course was on neuroscience, and our perspective was largely that the ideas are promising for applications to education but not likely to be in many schools for the foreseeable future. Although that analysis was based on looking at feedback as well as data collection, I think the basic tenants are the same.
However, data based on student work is an untapped goldmine of potentially useful data. From a math and science context, I think being able to see trends in student strengths and challenges would be helpful. Even with rubrics there are a lot of nuances that are summarized; if we were instead scanning all student work and use image recognition and AI to capture details of responses and feedback, we could potentially highlight aspects of problem solving skills that are useful in aggregate. If a student is frequently confusing trig ratios for example, this could be a factor in multiple contexts (trig identities, mechanics problems in physics, calculus, geometry, computer science). Marking an individual assessment it may be hard to tell if an isolated mistake is a one time slip or an underlying skill gap; this could be a way to address it.
Devon
Hello Devon,
Thanks for the feedback in terms of your insights with AI and how they may apply to education. Specifically in the realms of sciences, a paper by Wulff et. al. (2022) suggests that Large Language Models (LLMs) have an untapped potential that may be under development as we speak, in which “machine learning and natural language processing [could] provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods” (p. 490). That in essence, LLMs could be used to provide specific feedback in addressing Science learning gaps.
We think that for certainty, this is a very possible area in which the future of AI may move to in an educational standpoint, in which the quantification of student responses can help build better LLMs that in essence provide more specific and quicker personalized feedback for learning.
Reference:
Wulff, P., Buschhüter, D., Westphal, A., Mientus, L., Nowak, A., & Borowski, A. (2022/08//). Bridging the gap between qualitative and quantitative assessment in science education research with machine learning — A case for pretrained language models-based clustering. Journal of Science Education and Technology, 31(4), 490-513. https://doi.org/10.1007/s10956-022-09969-w
Hi Alan,
Thanks for the response. That article sounds interesting, I’ll have to give it a read.
Devon
Thanks to you both on a fantastic topic. The quantified self and mobility are so intertwined, it’s a really fascinating topic. I was surprised byhow many examples in your poll that I could think of, that were ways that I personally “quantify” myself.
“What are the potential mental health impacts of constantly monitoring and quantifying oneself?” This question immediately makes me think of the health and wellness applications of tracking. Movement goals of 20,000 steps are now the new 10,000 steps in many of my feeds, and it seems like many young people can easily fall into some disordered habits enabled by excessive tracking. Most fitness plans will recommend using food trackers like My Fitness Pal to track calories, even using your phone to scan barcodes on food items for macros. I can see how this constant pressure to hit these daily or weekly goals can slip into less healthy habits of restrictions and overexercising. These have always been persistent issues. However, having an app that rewards you for hitting your self-determined goals (healthy or not) is fairly new. The apps and trackers also tend to legitimize behaviour. I see the benefit of viewing data trends regarding activities, habits, and progress, but this needs to be tempered with the overload of minutiae data that is consistently collected, especially for young minds.
Thank you again for the great work!
Hi Nicole,
You raise some great points! I think on one hand quantifying one’s own activities and behaviours can be motivating, particularly when goals are met. There is also an aspect of pride and satisfaction when one can share these successes socially. On the other hand, this also creates a social pressure to share and to meet goals, which can quickly become de-motivating depending on the circumstance. As you mention, it’s particularly critical for young people (kids, teenagers, etc.) to understand that what is shared socially may be skewed to show more ‘successes’ than ‘failures’. Successes and met goals may be overrepresented online and therefore skew their idea of what is feasible / realistic.
Hey Nicole,
Thanks for the feedback. I definitely resonated with your thoughts in terms of over emphasizing of quantified data when it comes to health in which can be quite toxic towards sustaining good mental health when balancing the physicality of things. It reminds me that body dysmorphia is an issue for many millenials in which can be quite catastrophic for society that promotes this unsustainable belief that everyone can have the ‘beach body’ look 24/7 if they constantly monitor their calories through barcoding all their food and counting every single kilojoule in their diet.
However I feel that there is a big missed opportunity when it comes to using this health data for the purposes of promoting strong educational gains. Learning on a good diet is clearly a well supported and researched topic, but it is not used to ‘quanitfy’ the ‘self’ when it comes to 21st century education for our Gen Z and Alpha generation. That’s defintiely something we predict as an area which society will grow to, where fitness trackers will be used to monitor educational gains, for children in a way that hopefully does not impeach on their personal privacy, but provides them with quantifiable knowledge for educational gains that are personalized and meaningful.
Hi Alan, yes that would be be incredibly beneficial. Particularly in cases where children are facing food insecurity and not having enough to eat before or at school. As you say, this is critical to health and learning. Could data like this quantify this need and equalize access to healthy food for all? Northern communities face such challenges with access to fresh, healthy and affordable food. Do you potentially see a solution through data that could lead to policy changes? Fascinating topic guys! Thanks again
Thank you for your OER! I can echo some of the previous comments that this is a compelling, engaging presentation that I enjoyed.
How could you integrate quantified self-data into a learning activity? Do the data and tools already exist?
Many of our students are already wearing smartwatches, and are aware of the tracking that phones can do. We’ve compared step counts, distance traveled, daily screen time, and so forth. I primarily think of this as being an education in a healthy lifestyle. More and more I am orienting my classes towards a holistic lifestyle – learning as an integrated activity in life, examining ourselves and the world around us as ever-present factors. In that sense, I love the idea of self-data integrated into learning activities, and I want to learn more and try more in this regard.
What are some other benefits or opportunities of the quantified self? How has the use of self-quantification tools affected your daily life and decision-making?
I am quite enthusiastic about the benefits of the quantified self. I am aware of the potential pitfalls and criticisms, but personally, I like harnessing the data for building good habits. Knowing my movement patterns, sleep patterns, screen use, and more enables me to reflect on my habits and seek improvements. Discipline is still a critical piece, and the determination to make good decisions, however, I know that I a more likely to make those choices when I am faced with hard data. I’ve also recently dealt with a health concern, and both doctors that I worked with asked about my smartwatch use and the potential for us to obtain data that way. There are benefits and opportunities to using technology that provides self-data.
What are the potential mental health impacts of constantly monitoring and quantifying oneself?
This greatly varies from person to person. For me, this quanitifcation is exciting and encouraging. For others, this could easily become an obsession and anxiety-inducing. User beware!
Thanks for engaging with so many of our reflection questions!
I’m pleasantly surprised that your doctors both asked about your smartwatch use and saw the opportunity to leverage data that may already exist / leverage data-gathering tools that you already have access to. I hope your health is on the mend and the concern has been addressed.
I’m glad our OER got you thinking about how data about one’s self can be collected and integrated into learning activities. As I was researching materials for our OER, I thought this was an effective way to incorporate more data analysis into the classroom, while having students inherently more ‘invested’ as it is their own data that is being used. Do you have any ideas on how you may incorporate this data into learning activities that would be relevant for your courses and students?
Hello Steve,
Thanks for the feedback. One notable issue I find with quantified knowledge for students when using trackers such as fitbit and other health data is the championing of choice. A paper done in Sweden in testing if berry smoothies, if consumed, before a test, has a significant impact on their academic performance. This study was done by Rosander et. al. (2015) and is something I find extremely difficult for the average educator to get so minute in terms of data to be able to analyze. Additionally, in their later article, Rosander et. al (2017) mentioned about the issue of the tastiness of the active smoothie vs. the control smoothie, in such a way that mentions the inability to enforce an ‘all or nothing’ type eating habit when it came to these smoothies.
The studies done by Rosander et. al. (2015 & 2017) captures the difficulty behind quantifying data for children. When we have to factor in the perferential nature of choice amongst students, data gets messy quick. So even when it comes to fitness data for students, using a smartwatch, you will get the issue similarly to where my high school classmate may use a University-grade lab equipment to agitate their Tamogachi, just to outcompete her classmates.
I think the big idea that I got after completing this A2 project was that data needs to be contextualized for it to be meaningful. So ultimately if we ever increase the amount of accessible quantified data for students to self reflect, that the skill of contextualization is still relied on humanistic interactions, especially one that emphasizes a teacher-student relationship.
References
Rosander, U., Rumpunen, K., Olsson, V., Sepp, H., Rosander, P., & Wendin, K. (2015). Could a smoothie, rich in nutrients and bioactive substances, improve school performance?. In Annals of Nutrition and Metabolism (Vol. 67, No. Suppl. 1, p. 212).
Rosander, U., Rumpunen, K., Olsson, V., Åström, M., Rosander, P., & Wendin, K. (2017). Methodological considerations in a pilot study on the effects of a berry enriched smoothie on children’s performance in school. Food & Nutrition Research, 61(1). https://doi.org/10.1080/16546628.2017.1409063
Hi Shannon and Alan,
I also really enjoyed this OER. I found that the content was not too arduous but still informative. I appreciated the various cross-curricular suggestions and the slide deck on the first page that indicated the educational insights connected with quantifiable data. I personally have the Samsung Galaxy watch, it tracks my steps, takes my heart rate, tells me when I need to move and it tracks my sleep if I am wearing it at night. Working through your OER I answered some questions below.
Q: What educational insights would you be interested in learning about yourself? What data would be needed to derive these insights and is this data already available?
A: I always claim to be someone who works better in the evenings and under pressure. I wonder if that is true or not. I would assume the date needed would include heart rate, time of day, stress level, and blood pressure. I would need to establish a baseline and then I would expect to complete the same task in the morning versus the evening, on a time crunch versus not and compare the data with the outcome of the tasks. It would be an interesting way to identify what time of data students do their best learning.
Q: Who should own the data collected by self-quantification tools?
A: Data should be owned by the individual wearing the device. Just like with FOIP, anything that is said or written about someone belongs to them, quantifiable data should also belong to the individual.
Q: How can users ensure the accuracy and reliability of the data collected by technology?
A: Ensuring accuracy and reliability is a tricky question – I would assume updates are provided by the company and their programming is accurate.
Q: How has the use of self-quantification tools affected your daily life and decision-making?
A: My watch tells me when I have been sitting too long. And as someone who tends to hyper-focus on tasks, it helps support movement and breaks. My watch also tells me if I have moved enough in the day which has made me a bit more conscientious about my daily activity. It is still easy to ignore, I think I need to be zapped or something to avoid ignoring my notifications.
Q: What are the potential mental health impacts of constantly monitoring and quantifying oneself?
A: I worry about students who hyperfocus on their quantifiable data. What is intended to be supportive can turn into something detrimental like disordered eating and over-exercising. Education about healthy habits and boundaries would be very important to maintain.
Thank you again for your OER!
Hi Jeannine,
Thanks so much for engaging with so many of our reflection questions!
Regarding the first question and you being someone who works better in the evenings and under pressure, I wonder if data around your activity (e.g. steps) or sleep have anything to do with it too. For example, maybe after a physically busy day, you are ready to sit down / ‘settle down’ and that’s when you find yourself to be most productive when it comes to work. In regards to sleep, according to Gartenberg (2023) with SleepSpace, humans typically experience peak alertness around 2-3 hours awakening and then again around 9-10 hours after awakening. Perhaps your most productive time falls within that 9-10 hours after awakening period!
Gartenberg, D. (2023, March 30). The circadian rhythm. SleepSpace. https://sleepspace.com/circadian-rhythm-2/#:~:text=A%20typical%20circadian%20rhythm%20in,9%20AM%20in%20the%20morning.
Hello Alan and Shannon,
Amazing OER! The site was designed well for engagement, self-thought, and reflection upon our quantified self. This is one of my favourite questions that you both had provided: How could you integrate quantified self data into a learning activity? Do the data and tools already exist?
This is an interesting question as we used to old-school log very basic data such as daily reading, sign-up for classroom jobs, home-school communication logs, and self-regulation reflections. It seems that we have been collecting data from students all this time but never identified it as quantified self data. I think this new generation of teaching actually lacks student initiatives to allow for more self quantification. It supports students in their accountability, formulating routines, and the process of or competing what was asked at school to follow through at home. From a behaviouralist or learning support perspective, a lot of students need these types of opportunities to do “better.” What I mean is that there seems to be a gap and a lack of awareness in how late students stay up, how little time is spent on review/individual learning or studying, sports, various activities that promote self-efficacy. I would use quantified self data to help with self-regulation, supports with existing behaviours, and homework activities that promote students’ likeliness of building better studying habits in preparation for classes.
For example, assigning a 5-10 minute YouTube video for homework and to complete the graphic organizer. The kicker, students have the option to watch the video from 1 to 10 minutes. Based on their selection, how much have they learned and their ability to complete the questions/worksheet. This is to make them think a little bit and to hold them responsible for their learning for the next class.
I loved this question as it made me think about how I can teach social studies better!
Hi Clare,
This is awesome! I’m so glad that one of our prompts got you thinking about your classroom and how you’ve related some of your ‘old-school log activities’ to the quantified self movement.
If your social studies class involves topics of cultural phenomena or social patterns, I wonder if you can even extend the activity and somehow use the class’s collective data for an activity.
Hi Shannon and Alan,
Thank you so much for your OER; it is very well-organized and extremely informative. It really got me thinking about where education is headed, and the positives and negatives that will result from advancements in the quantified self. I especially appreciated your video selections and your reference to Gattica; they point out in such a great way how people–especially young learners–can become as efficient as possible, kind of like fine-tuning machines. I would like to direct my response in this area, that in treating humans as machines (or at least quantifying and analyzing them) we have an amazing amount of positive and negative results.
First, I would like to answer your question: What are some other benefits or opportunities of the quantified self?
One amazing example is how a teacher would be able to be preemptive in their instruction through being able to observe information on things like attention and heart-rate. At a critical point, a teacher would be able to get a class or individual to take a break before it would become a larger issue. I often imagine my classroom as a car, operating as best it can. There may be problems with the car, but I never know until the problem becomes apparent, usually at a stage where the part breaks, falls off, shuts down, starts to make a really loud noise, or starts smoking. When we transfer this comparison to a classroom, we definitely see the problem when students start to fall off, break, shut down, or smoke. I hope it is a more humourous comparison but the truth remains that I, as an observer of almost 30 learners, am doing my best to guess at what learners need and often do not know there is a problem until it becomes so big that it is noticeable. By the time I observe it, it is often too late. So, having a quantified classroom that would alert me when a problem is emerging, before breaking point, would be extremely helpful. “Oh! I see that John/Jane is getting a raised heart rate, maybe I could encourage them to go for a walk before they get too frustrated and leave the school grounds again.”
Second: What are the potential mental health impacts of constantly monitoring and quantifying oneself? Your OER and video choice presented this concept so perfectly but I would just like to raise the discussion of what that would actually be like for a learner. In thinking of the example above, especially in monitoring heart rate, imagine that every time you were scared or frustrated, someone would know, even if you were trying really hard to keep yourself under control. Imagine if you were the only one who showed data amongst your peers that would indicate you were embarrassed. Imagine always being attached to a lie detector when you are on school property. These ideas may be extreme, but they are all some of the ways that heart rate data alone could be used in a quantified way. With this type of constant and intense surveillance, students are not even “safe” inside their own heads anymore. An example of not being safe in your own head, and the consequences which follow can be watched here, from the film you have highlighted, Gattica (1997): https://www.youtube.com/watch?v=SISq9xaC7LA
Looking at these explicit examples of positive and negative use of heart rate, I believe what determines whether the quantification of learners is positive or negative would be how the information is used, and who it is used by. Educators and developers of this technology have an extremely important responsibility to make learners feel comfortable, supported, and private. It will take great care and patience in order to record and use quantifiable information of youth.
Can you think of any other issues, solutions, or required guidelines for these two examples to make them both more positive?
Thank you again for a job well done!
Kevin
Kairos: A Journal of Rhetoric, Technology, and Pedagogy. (2018, May 7). “Gattaca (Experiment 1)” by Sean Zdenek (Kairos 23.1 Topoi). YouTube. https://www.youtube.com/watch?v=SISq9xaC7LA
Hi Kevin,
I’ve never heard anyone compare their classroom to a car operating smoothly, but I like it! You’re right that as a teacher, we are often guessing at what learners need and even then, we may misinterpret physical symptoms or expressions. Quantified data may be a more objective way to be alerted and allow us to act before problems arise.
Great points around the potential mental health impacts of constantly being monitored. I wonder how many teachers would be willing to have their heart rate monitored and ‘on display’ for students – could this help to normalize the sharing of data and create a ‘safer’ space for students, as the teacher is also vulnerable?
In terms of how to make your example more positive, do you think the learner would feel less embarrassed if only the teacher saw the heart rate data? The teacher could then work one-on-one with the learner to develop personalized strategies. Alternatively, to ensure learners maintain control over their data, perhaps they are the only ones to analyze it. This way, they can still leverage the benefits of quantified data, but do not have to feel embarrassed as the data is kept private.
Hi Shannon and Alan,
This was interesting. I was struck especially by your two videos included in the OER.
In the first, from 2015 (New Scientist) it struck me that while technology has continued forward in this area of personal bio data collection in the last 9 years, I don’t feel that widespread adoption has taken place. With the exception of everyone seeming to want to measure their 10,000 daily steps, I don’t find most people I know are tracking other data. A few friends are really into tracking sleep. I wonder why this is? Is it for a lack of interest on our part or has the technology not been packaged and sold to us in an attractive way? I’ve never had a doctor suggest to me, that hey, its good practice to track your bio markers, but that would seem to make a lot of sense. I found an interesting paper on how far technology has come, including data tracking ingestibles for medical purposes: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185336/
In the second video, (The Wall Street Journal, 2019), wow, I had not heard of any schools implementing this kind of experiment with EEG tracking devices. Jokingly I thought, they could totally hook that up to a little electrical shock when the kids fall asleep. But seriously, I definitely respect the Chinese push forward in this area to take seriously experimenting with new possibilities and opportunities it is so interesting.
I’m interested to answer 2 of your questions:
Reflection Question:
Q: What educational insights would you be interested in learning about yourself? What data would be needed to derive these insights and is this data already available?
A: I love to take part in research in which participants do testing and tasks after various lengths of sleep to see how that correlates with your cognitive functioning. In other words, I’d love to know my optimal sleep times and length for optimal performance.
Q: Reflection Question:
Who should own the data collected by self-quantification tools?
A: Ideally, I’d love to see a user centered secure data bank, like the iPhone philosophy. Then it would be great if you could access your own data, put it through a privacy scrubber on the device and then share it with 3rd parties such as medical, educational or apps etc.
Great job on your OER. Fun topic.
~Rich
Hey Rich,
Appreciate the feedback and I definitely agree with the puzzling complexity of why individuals do not necessarily track their data more consistently for health purposes. I do know though that in the fitness and athletic industry, there is a focus on number of reps, total load volumes, swim speed, and other quantifable data. When it comes to competitions and sport, quantified data is profuse in that sector, which I would say is something that does currently exist in terms of ways we measure health.
Aside from that, there is the notable trends in smart watches to including such data, and I am for one have bought into this trend of tracking my own personal biodata using existing technology that collects, aggregates, analyzes, and presents this data to me. It may come at a level of risk of privacy, but I do feel that there is more benefits than cons.
In terms of sleep research, the Canadian Sleep Research Consortium may be a good starting point. For personalized data for yourself that are available on the market, I would recommend some of the Garmin technology in which they use a relatively new ‘body battery’ meaurement feature that I am very intrigued by and have been monitoring closely since purchasing my watch. (Please note that this is a commerically available technology that may not have sound academic research supporting its claims, and how you choose to interpret the data is your responsibility.)
I definitely also agree, that a centralized secure data bank would be fantastic to have, and does also lean into a personalized Internet of Things, similar to the past week’s topic.
Hi Rich,
Thanks for your feedback!
On the topic of sleep and cognitive functioning, have you ever tried any of the existing apps (e.g. Sleep Cycle or SleepScore) to learn more about your sleep patterns? Often times, even with a reasonable amount of hours of sleep, I will feel groggy and less rested if it feels like I’ve woken up from a ‘deep sleep’. These apps may help to track your sleep patterns and Sleep Cycle as an example is “designed to gently wake you up while you’re in your lightest sleep phase” (Sleep Cycle, n.d.). The Cleveland Clinic (2023) discusses our stages of sleep and how we enter and cycle between rapid eye movement (REM) and non-REM sleep cycles after falling asleep. The article discusses how waking up during a Stage 3 non-REM sleep cycle may leave us feeling confused or having mental fog. I wonder whether feeling a certain way (groggy, confused, mental fog) is just ‘superficial’ or whether there is an impact on our cognitive function. All of this would be interesting to explore further but as a first step, it may be worth checking out and using some of the sleep apps available to gain some preliminary information about your sleep!
Apps to explore:
Sleep Cycle: https://apps.apple.com/us/app/sleep-cycle-tracker-sounds/id320606217
SleepScore: https://apps.apple.com/us/app/sleepscore/id1364781299
References
Cleveland Clinic. (2023, June 19). Sleep. https://my.clevelandclinic.org/health/body/12148-sleep-basics
Sleep Cycle AB. (n.d.). Sleep Cycle – Tracker & Sounds. App Store. https://apps.apple.com/us/app/sleep-cycle-tracker-sounds/id320606217