Data
Started a new statistics course. I feel statistics to be highly offputting, and doing this course I understand why. There is so much categorization! Data can be qualitative/quantitative. Measurements come in levels. And then a list there. And as I furiously scribble this down to try to remember, I wonder why this is so. That is, why so many lists and classifications. I will try to work out a way of presenting this in a more fun way.
Physical
Actually, this week I cheat. I decided to take a break from my quest to find the origins of exercise recommendation to read an article on the compliance issue. The article, The exercise–affect–adherence pathway: an evolutionary perspective, is really a psychology article. Compliance is a big problem- it is extremely hard to convince people to exercise. The article tries to answer "why do people not like exercising?" And come up with way to increase exercise adherence.
Why do people not like exercising? In a word- lazy. If no short term benefit, then people won't do it (which is why, they say, cognitive convincing won't work). And then they write a lot, and come to the conclusion that to increase adherence there should be a direct benefit.
This was my first psychology article ever, so I should be more tolerant, but wasn't this rather an obvious conclusion? It isn't new that if people like exercising, they will exercise. And it also isn't new that a possible path to convicing is to make activity a necessity. And it really isn't even close to new (just read one of the old articles from the 90s I posted previously) that an example of activity-necessity is walking to work (or, at least, park a block away and walk for a bit). It is possible that in the sea of technical jargon they said something, but I struggle with hypotheses on no scientific basis, particularly when there are so many words that mean nothing to the extra-psychology world. "Negative affective response..." I guess this would make it qualitative data (people really don't like it) except there was no data.
Interestingly, this article mentioned that "5.3 million people die globally each year due to lack of PA". (PA-physical activity) I really must read these articles where they make these estimations!
Saturday, 23 March 2019
Saturday, 16 March 2019
End of course 1
I keep forgetting to time myself. I keep forgetting to click on the website to see how much time I actually spend programming. As a result, for now, I will let it go. I believe I spend a few hours, but that just means more than one and less than seven (seven seems more than few, doesn't it?).
Data
I finished the first course! It feels like a stepping stone towards a goal.
On to the next course: Foundations of Data Analysis on edX. I quite like edX. More than liking edX, I am excited to learn Statistics. I've always regarded statistics as the unfortunate sibling to math. The not as pretty, not as smart, not as kind, not as funny sibling. And yet, I am excited for this course. I am excited to (hopefully) fall in love with the subject, I am excited to learn new topics, and I am excited because it will allow me to do what I really want to do on this blog: mix two subjects that I love. I hope to bring examples from one to illustrate the other.
Physical
This week I keep moving back into the history of articles written, with the 1992 How Much Physical Activity is Good For Health. This article is well written but more of the same in terms of outlining the problem and why exercise is a solution, which is to be expected with a review. While reading it, I realize how vague the question "how can I get the most out of activity" really is. Especially when viewed from a macro perspective. People (and a sizable number of people, too) just don't seem to move. Thus, instead of one vague question there are two:
1. How can I get the most out of activity
2. How can the population lower effectively (read: minimal commitment) negative phenomena by being more active?
For the first I have not yet found any hints.
For the second I have not yet found any hints.
I have, however, learnt this: neither has anyone else. That is oddly comforting.
Data
I finished the first course! It feels like a stepping stone towards a goal.
On to the next course: Foundations of Data Analysis on edX. I quite like edX. More than liking edX, I am excited to learn Statistics. I've always regarded statistics as the unfortunate sibling to math. The not as pretty, not as smart, not as kind, not as funny sibling. And yet, I am excited for this course. I am excited to (hopefully) fall in love with the subject, I am excited to learn new topics, and I am excited because it will allow me to do what I really want to do on this blog: mix two subjects that I love. I hope to bring examples from one to illustrate the other.
Physical
This week I keep moving back into the history of articles written, with the 1992 How Much Physical Activity is Good For Health. This article is well written but more of the same in terms of outlining the problem and why exercise is a solution, which is to be expected with a review. While reading it, I realize how vague the question "how can I get the most out of activity" really is. Especially when viewed from a macro perspective. People (and a sizable number of people, too) just don't seem to move. Thus, instead of one vague question there are two:
1. How can I get the most out of activity
2. How can the population lower effectively (read: minimal commitment) negative phenomena by being more active?
For the first I have not yet found any hints.
For the second I have not yet found any hints.
I have, however, learnt this: neither has anyone else. That is oddly comforting.
Saturday, 9 March 2019
Classes and Exercise
Data
Finished week four of the course, about to begin week five- the last week of the course. I do love programming, and the course (U of T's Learn to Program: Crafting Quality Code) is set up cleverly. Rather than teaching lists (no, no pun) of commands and functions, there is a nice flow that allows more intuition. This week was classes, and something about it reminds me of games. Programming, as other language learning (and especially pure math), has this fun element of structure. In the total rigidity and definition there is an opportunity to bring forth your own personality. The clear boundaries suit me, and I find thinking of clever ways to program highly satisfying. I do wish there was more programming, though. I also hope to finish week five this week, and thus start the next course at the beginning of next week.
Physical
All that I love about math is the inverse of what I love about the human body. Or, to give a better example- I love to learn about the heart and the brain. The heart is amazing in its simplicity, and the brain in its complexity. The heart isn't necessarily simple, but it is rather straightforward. And the brain is a mess.
My first goal is trying to understand which exercise recommendation I should give to someone who could do anything. Or, simply put, what is "best". That sounded so simple, and yet is such a marvellous mess! The recommendation today is, officially:
150 minutes of moderate intensity aerobic or 75 minutes of high intensity aerobic
2 days of muscle strengthening
Flexibility
Balance
I wanted to find the original exercise recommendation, but finding the first is a challenge. As one article cites the preceding, which cites its predecessor (rather than all just quoting the first that said "30 minutes of moderate aerobic activity"), in a long train, I arbitrarily stopped in 1995, at R.R. Pate et al's Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. A challenge I always have is to not look up tangent points (such as the highly enticing "250,000 deaths are due to lack of physical activity"). Sticking to the main points- their goal was to identify which exercise to promote and how to promote it.
This article was written, quite clearly, before resistance exercise began to compete for attention.
The recommendation: 30 minutes of moderate physical activity (note- not exercise. Gardening counts), preferably daily. The reason for moderate physical activity- so that people will stick to it. Activity and not exercise- more of the same, and so even doing an activity in bouts of ten minutes will suffice. This brings up the basic question of- if someone can do high intensity- is it "better"?
I realize that asking better brings me back to my aforementioned love of clean conclusions, which exercise is clearly not. The benefits from exercise are so widespread that one exercise recommendation can't be superior to another in every way.
Isn't that a shame!
But new questions arise:
1. Original article that states that moderate is on par with high intensity.
2. I am so tempted to write "ten minute bouts" but that has recently been debunked.
3. How did they get to the dose? Why 30 minutes?
I also began another qrticle (or, really, book) from the same year, and hope to report more on it next time. My ambition had been two articles a week. For now, one will have to suffice.
Finished week four of the course, about to begin week five- the last week of the course. I do love programming, and the course (U of T's Learn to Program: Crafting Quality Code) is set up cleverly. Rather than teaching lists (no, no pun) of commands and functions, there is a nice flow that allows more intuition. This week was classes, and something about it reminds me of games. Programming, as other language learning (and especially pure math), has this fun element of structure. In the total rigidity and definition there is an opportunity to bring forth your own personality. The clear boundaries suit me, and I find thinking of clever ways to program highly satisfying. I do wish there was more programming, though. I also hope to finish week five this week, and thus start the next course at the beginning of next week.
Physical
All that I love about math is the inverse of what I love about the human body. Or, to give a better example- I love to learn about the heart and the brain. The heart is amazing in its simplicity, and the brain in its complexity. The heart isn't necessarily simple, but it is rather straightforward. And the brain is a mess.
My first goal is trying to understand which exercise recommendation I should give to someone who could do anything. Or, simply put, what is "best". That sounded so simple, and yet is such a marvellous mess! The recommendation today is, officially:
150 minutes of moderate intensity aerobic or 75 minutes of high intensity aerobic
2 days of muscle strengthening
Flexibility
Balance
I wanted to find the original exercise recommendation, but finding the first is a challenge. As one article cites the preceding, which cites its predecessor (rather than all just quoting the first that said "30 minutes of moderate aerobic activity"), in a long train, I arbitrarily stopped in 1995, at R.R. Pate et al's Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. A challenge I always have is to not look up tangent points (such as the highly enticing "250,000 deaths are due to lack of physical activity"). Sticking to the main points- their goal was to identify which exercise to promote and how to promote it.
This article was written, quite clearly, before resistance exercise began to compete for attention.
The recommendation: 30 minutes of moderate physical activity (note- not exercise. Gardening counts), preferably daily. The reason for moderate physical activity- so that people will stick to it. Activity and not exercise- more of the same, and so even doing an activity in bouts of ten minutes will suffice. This brings up the basic question of- if someone can do high intensity- is it "better"?
I realize that asking better brings me back to my aforementioned love of clean conclusions, which exercise is clearly not. The benefits from exercise are so widespread that one exercise recommendation can't be superior to another in every way.
Isn't that a shame!
But new questions arise:
1. Original article that states that moderate is on par with high intensity.
2. I am so tempted to write "ten minute bouts" but that has recently been debunked.
3. How did they get to the dose? Why 30 minutes?
I also began another qrticle (or, really, book) from the same year, and hope to report more on it next time. My ambition had been two articles a week. For now, one will have to suffice.
Monday, 4 March 2019
Algorithms
Data
Yesterday I finished week three of the computers course. This week focused on algorithms: one "session" on comparing different searching algorithms with a focus on computation time and the other compared different sorts of algorithms in sorting.
Apart from the intense fun in this kind of thing, I realized that this blog can marry quite a few loves:
1. Math. OK, statistics. I like playing with math, quite a lot even. And though statistics really isn't math, playing with numbers, understanding concepts and explaining them- that's really quite a bit of fun. Algorithms falls into this category.
2. Understanding how things work: If someone were to ask my true life's dream, the unattainable, unrealistic dream I would say "I want to understand how the brain works." That is not possible. Not from a maximum of an hour a night, cramming time in when I can to learn and progress. But so much has been written on exercise, and a moving body is interesting. Why do we need to move? Since so much has been written, I would love to put something together over here, collect articles, and patch myself through towards an understanding. And explain the statistics each article used.
3. I just really, really love to learn. Like a new love, like an old love. I get excited reading good books, my heart pounds upon listening to good lectures. And how I love that brief moment of understanding how 2 puzzle pieces in the infinite puzzle of physics, Biology, etc click together. It's a thrill I can't explain. Way better than swimming, skydiving and cheesecake.
Yesterday I finished week three of the computers course. This week focused on algorithms: one "session" on comparing different searching algorithms with a focus on computation time and the other compared different sorts of algorithms in sorting.
Apart from the intense fun in this kind of thing, I realized that this blog can marry quite a few loves:
1. Math. OK, statistics. I like playing with math, quite a lot even. And though statistics really isn't math, playing with numbers, understanding concepts and explaining them- that's really quite a bit of fun. Algorithms falls into this category.
2. Understanding how things work: If someone were to ask my true life's dream, the unattainable, unrealistic dream I would say "I want to understand how the brain works." That is not possible. Not from a maximum of an hour a night, cramming time in when I can to learn and progress. But so much has been written on exercise, and a moving body is interesting. Why do we need to move? Since so much has been written, I would love to put something together over here, collect articles, and patch myself through towards an understanding. And explain the statistics each article used.
3. I just really, really love to learn. Like a new love, like an old love. I get excited reading good books, my heart pounds upon listening to good lectures. And how I love that brief moment of understanding how 2 puzzle pieces in the infinite puzzle of physics, Biology, etc click together. It's a thrill I can't explain. Way better than swimming, skydiving and cheesecake.
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