It's been a long, busy time. I hope I am now back to my once a week posting- both in terms of advancing and in terms of article reading!
I finished the two statistics courses, and the lesson learnt is: What you get out of a study depends on who does the statistics. It is really easy to massage data by taking out outliers. It is easy to get effects by choosing which test you use. And since so much is up to the discretion of the researcher, it is hard to be able to be critical enough to not trust the study, and not cynical enough to be able to reach conclusions that make sense. Or, reworded- throwing out research is the easy solution, but not a smart one.
I have great news- I began an algorithms course, and then I applied (and won!) a two month scholarship to Dataquest. Dataquest is a website that teaches data skills in R and Python. It's a great system, where you can study for ten minutes at a time. I decided to apply to a diversity scholarship (for women etc) on a whim, and was really lucky to get it. I now have two months to finish as much as I can. And yet, I want to stick with the algorithms course. The trick is to see it as a positive challenge, but it is fun, and I am enjoying it thoroughly.
On to the physical part. This week's article (Beneficial associations of low and large doses of leisure time physical activity with all-cause, cardiovascular disease and cancer mortality: a national cohort study of 88,140 US adults by Zhao et al) is another how much exercise should we do article. They took a large cohort, examined 88,000+ adults between 40-85, examined how many died in later surveys of cardiovascular disease and cancer-related deaths, and then calculated a hazards ratio and all that stuff. They found that even 10-59 minutes of moderate activity (read:walking) a week is enough to reduce risk (18%, no less). And the more you do, the stronger the effect, to a point. 10 minutes a week! That is going to the bathroom time. That's nothing. There are a number of possibilites:
1. This is absolutely true.
2. This is a poorly done study where the preconceived notions dictated the results.
3. Something in between. Well, obviously.
The 18% comes from the 0.82 hazard ratio- hazard rate (dying at a certain time) of those doing exercise/those not. 0.82 means smaller risk. And the exercise questionnaire was given only initially-over 36,000 did NO activity. How could people do no activity? 10 minutes a week! This is the problem with questionnaires.
All the studies I've looked at show trends- just be a little active. But the results they show seem highly simplistic. And don't take into account population differences.
Wednesday, 29 May 2019
Saturday, 13 April 2019
Significance
This past week (I am not certain how to define week anymore) the course focused on bivariate statistics. That is, the nature of the relationship of two variables. Or, a more precise "that is"- does a change in one variable lead to a change in the other? Does smoking more cigarettes lead to a higher chance of causing something (quantitative) and does being female result in being smarter (qualitative)?
This ties in nicely with statistical significance. Haaf et al, Retire significance, but still test hypotheses really want to be done with significance. So much so, that they got 800 scientists to sign that they also want to be done with significance. Significance means that the results are more than just chance. Or so I thought.
In the article, they blame scientists for using significance as a kind of pass/fail criteria. As a result of significance being the end-goal, they claim scientists often massage data and cherry pick methods so as to prove significance. Finally, the arbitrary level of 0.05 shouldn't be used blindly.
The solutions they propose are: pre-register studies and print all results, don't be so confident and categorical but rather try to think through the data and realize uncertainty is built in (they reccomend "compatible intervals" rather than confidence intervals), calculate a p value accurately rather than blindly using 0.05 and be humble.
As scientists are known for their ability to be humble, I doubt the last recommendation should be difficult. And I really do agree that studies should be pre-registered. But the big problem is as follows: significance serves to add objectivity and steer clear from intuition. Of course it would be wonderful if people were thoughtful and clever and objective. And while many people can be thoughtful, really are clever, and try to be objective, I doubt significance is the problem. After all, if those who used significance incorrectly weren't able to learn what significance (a relatively not challenging term) means, why would they take the time to do more complicated statistics? Intuition is a big problem (as the reproducibility problem proves), and weeding out significance is just a cosmetic solution.
I realize non of this is physical and all of this is data... Oh well.
This ties in nicely with statistical significance. Haaf et al, Retire significance, but still test hypotheses really want to be done with significance. So much so, that they got 800 scientists to sign that they also want to be done with significance. Significance means that the results are more than just chance. Or so I thought.
In the article, they blame scientists for using significance as a kind of pass/fail criteria. As a result of significance being the end-goal, they claim scientists often massage data and cherry pick methods so as to prove significance. Finally, the arbitrary level of 0.05 shouldn't be used blindly.
The solutions they propose are: pre-register studies and print all results, don't be so confident and categorical but rather try to think through the data and realize uncertainty is built in (they reccomend "compatible intervals" rather than confidence intervals), calculate a p value accurately rather than blindly using 0.05 and be humble.
As scientists are known for their ability to be humble, I doubt the last recommendation should be difficult. And I really do agree that studies should be pre-registered. But the big problem is as follows: significance serves to add objectivity and steer clear from intuition. Of course it would be wonderful if people were thoughtful and clever and objective. And while many people can be thoughtful, really are clever, and try to be objective, I doubt significance is the problem. After all, if those who used significance incorrectly weren't able to learn what significance (a relatively not challenging term) means, why would they take the time to do more complicated statistics? Intuition is a big problem (as the reproducibility problem proves), and weeding out significance is just a cosmetic solution.
I realize non of this is physical and all of this is data... Oh well.
Sunday, 7 April 2019
Statistics and Compliance, II
This week (if a week is defined as "since last time I posted") I learned that in case of a normal distribution, the mean/standard deviation is to be used. However, if the data is skewed, median/five number summary is to be used. I expect the latter (where median is the middle data) is better in skewed populations since it will be less affected by extreme cases. However, if you have two studies, and one gets results that are skewed while the other has a nice, normal distribution- how can you compare?
I realize this: I have never, ever ever ever seen the median being used to describe a population. I would say "and IQR even less so" but you can't have less than zero. Weirdly, I HAVE seen articles where the mean-standard deviation gives a value less than zero for something that can't be less than zero (pain scale, for example).
Fortunately, the article I read was great for continuing to wonder how to compare articles, and also continues the article from last week (again, week meaning last blog post). The article is Barriers to Treatment Adherence in Physiotherapy Outpatient Clinics: A Systematic Review by Jack et al.
The article narrows hundreds of potential articles to 20 cohorts and aims to identify barriers to adherence.
The punchline is that most studies found a low level of physical activity predicted low adherence to physical activity. Then a whole bunch of psychological reasons (depression, anxiety, etc), low social support and finally pain. The place of psychology in physical therapy/exercise is fascinating, as CBT and mindfulness courses seem to be creeping. But back to the main point. If you don't exercise, you probably won't exercise. I can't decide if that's wholly obvious to the point of silly, or really depressing to the point of depressing. We should start paying people to exercise.
And now for the technical aspect of comparing wildly different studies. What a fun mess! How do you define adherence? What is the lack of adherence percentage? Not known. Though the latter is anywhere between 14$-70% or above or below. And this was just in the introduction! The studies are so completely different in population type, size, and methods. Population type- they examined very different types of physical therapy such as pelvic floor, sports injury and osteoarthritis. The populations size in the studies varied extremely by size; one had 34 participants. Most had more.The methods were mostly questionnaire, but obviously different questionnaires, and thus comparing statistics really isn't quite right. Any guesses on how many studies used the median?
I realize this: I have never, ever ever ever seen the median being used to describe a population. I would say "and IQR even less so" but you can't have less than zero. Weirdly, I HAVE seen articles where the mean-standard deviation gives a value less than zero for something that can't be less than zero (pain scale, for example).
Fortunately, the article I read was great for continuing to wonder how to compare articles, and also continues the article from last week (again, week meaning last blog post). The article is Barriers to Treatment Adherence in Physiotherapy Outpatient Clinics: A Systematic Review by Jack et al.
The article narrows hundreds of potential articles to 20 cohorts and aims to identify barriers to adherence.
The punchline is that most studies found a low level of physical activity predicted low adherence to physical activity. Then a whole bunch of psychological reasons (depression, anxiety, etc), low social support and finally pain. The place of psychology in physical therapy/exercise is fascinating, as CBT and mindfulness courses seem to be creeping. But back to the main point. If you don't exercise, you probably won't exercise. I can't decide if that's wholly obvious to the point of silly, or really depressing to the point of depressing. We should start paying people to exercise.
And now for the technical aspect of comparing wildly different studies. What a fun mess! How do you define adherence? What is the lack of adherence percentage? Not known. Though the latter is anywhere between 14$-70% or above or below. And this was just in the introduction! The studies are so completely different in population type, size, and methods. Population type- they examined very different types of physical therapy such as pelvic floor, sports injury and osteoarthritis. The populations size in the studies varied extremely by size; one had 34 participants. Most had more.The methods were mostly questionnaire, but obviously different questionnaires, and thus comparing statistics really isn't quite right. Any guesses on how many studies used the median?
Saturday, 23 March 2019
Statistics and Compliance
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!
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, 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.
Wednesday, 27 February 2019
Week 1
I am not yet certain how exactly I want to set up this blog, so we'll see as we go. Which is to say, I'll see as I go.
Challenges:
1. The ever present challenge of sitting on programming every day. Two days I physically didn't get to my computer, and thus didn't program. Friday was great, and programmed a lot. I will have to hope that what I manage to do will settle well with me. I always feel behind in everything else, there is no need to feel behind here. And there is the philosophical "behind what" but I won't get into that kind of thing.
2. Toggl. I decided to track how much time I program. Most days I forgot to turn on the timer to track how much time I program. The website I use is Toggl, and it's flashy (the opposite of a simple timer), but I forget to actually GO to the website. The funny thing is, I often remember midway. But kind of like dieting- I alREAdy forgot the first part, may as well just not monitor. Must improve.
3. Figuring out how many articles to read for physiotherapy/exercise. More on that later.
Computers
Toggl: 2 hours and 11 minutes. Reality- more. No idea how MUCH more, but more.
I don't think I mentioned the program. I have programmed (a bit) in python before. I decided to do the second course in University of Toronto (yay)- Learn to Program on coursera. I feel lacking in confidence and have taken so many elementary courses (how many times can I learn what a string is?!) that it's really nice to do debugging. I am on the assignment of week 2. Of course I keep doubting myself. But I have a long program ahead of me, and I just have to assume self doubt will continue to be an old friend throughout.
Physical Therapy/ Activity
No idea how to do this. I read an article this week:
Challenges:
1. The ever present challenge of sitting on programming every day. Two days I physically didn't get to my computer, and thus didn't program. Friday was great, and programmed a lot. I will have to hope that what I manage to do will settle well with me. I always feel behind in everything else, there is no need to feel behind here. And there is the philosophical "behind what" but I won't get into that kind of thing.
2. Toggl. I decided to track how much time I program. Most days I forgot to turn on the timer to track how much time I program. The website I use is Toggl, and it's flashy (the opposite of a simple timer), but I forget to actually GO to the website. The funny thing is, I often remember midway. But kind of like dieting- I alREAdy forgot the first part, may as well just not monitor. Must improve.
3. Figuring out how many articles to read for physiotherapy/exercise. More on that later.
Computers
Toggl: 2 hours and 11 minutes. Reality- more. No idea how MUCH more, but more.
I don't think I mentioned the program. I have programmed (a bit) in python before. I decided to do the second course in University of Toronto (yay)- Learn to Program on coursera. I feel lacking in confidence and have taken so many elementary courses (how many times can I learn what a string is?!) that it's really nice to do debugging. I am on the assignment of week 2. Of course I keep doubting myself. But I have a long program ahead of me, and I just have to assume self doubt will continue to be an old friend throughout.
Physical Therapy/ Activity
No idea how to do this. I read an article this week:
Short and sporadic bouts in the 2018 US physical activity guidelines: is high-intensity incidental physical activity the new HIIT? By Stamatakis et al.
Ugh.
The article was in response to the new exercise guidelines that recommended just doing anything. Anything. Forget aerobic. Forget resistance. Forget HIIT. Forget balance. Just whatever. Whatever you do is great. Get your heart rate up, but it doesn't matter for how long. It doesn't matter doing what. Cleaning? Great. Groceries? Great. Yeah high intensity (though that also feels like it may change).
To be fair, I should read the guidelines. And I will. But all the confusing recommendations change (and I believe the same applies to nutrition) leaves me at a loss for what I am meant to do. And what do I tell others? It's frustrating tor read another in a long list of articles that aren't quite based on research, or on quality research.
But. But it feels like people aren't moving, and now research (though haven't SEEN the research and I don't trust anyone. I'll quote it, but don't trust it. Yet) is saying that the lack of activity is what matters. Is this an attempt to basically nudge people into action? The recommendations are slowly throwing their hands in the air saying please, please just do anything but sit there.
Are people that lazy that they do so little of anything that walking a set of stairs is novel? I am clearly disconnected.
And that was my week!
But. But it feels like people aren't moving, and now research (though haven't SEEN the research and I don't trust anyone. I'll quote it, but don't trust it. Yet) is saying that the lack of activity is what matters. Is this an attempt to basically nudge people into action? The recommendations are slowly throwing their hands in the air saying please, please just do anything but sit there.
Are people that lazy that they do so little of anything that walking a set of stairs is novel? I am clearly disconnected.
And that was my week!
Thursday, 21 February 2019
Beginnings
I want to study. I want to study so I can change my job's focus. I want to study to have a better understanding. But I really want to study because I love studying.
Over the past two years, when my time has suddenly started feeling much more precious, I began to notice a strong lack of conversation (or, long conversation) that are intellectually invoking. I felt this way throghout my studies in physical therapy (and not just), but time seemed to be abundant and I could easily fill it as I chose. Naturally, I didn't choose to challenge myself, but that's another matter.
I finally decided this week to begin studying a string of courses in order to be proficient in data science. Trendy, yes, but I also feel that in my field (physical therapy in particular, sports/subjects related to the body) it is truly lacking. I love math. I love modeling (the math kind, obviously). Why not be presumptuous about my abilities and try to make my way through courses, so as to be able to hopefully pursue research that interests me?
And so I began by writing a curriculum for myself.
Here I plan to post updates on my courses, and perhaps review articles that interest me. I am going to use the pomodoro technique, so will write actual time spent.
As for the curriculum: I have a bit of programming in my background (Matlab, Python) but don't feel strong enough. I also am realistic that courses will cost money. I decided to audit the intro courses, and pay for advanced courses. This way I will get to learn and review at the beginning, and get certificates for more advanced courses.
Over the past two years, when my time has suddenly started feeling much more precious, I began to notice a strong lack of conversation (or, long conversation) that are intellectually invoking. I felt this way throghout my studies in physical therapy (and not just), but time seemed to be abundant and I could easily fill it as I chose. Naturally, I didn't choose to challenge myself, but that's another matter.
I finally decided this week to begin studying a string of courses in order to be proficient in data science. Trendy, yes, but I also feel that in my field (physical therapy in particular, sports/subjects related to the body) it is truly lacking. I love math. I love modeling (the math kind, obviously). Why not be presumptuous about my abilities and try to make my way through courses, so as to be able to hopefully pursue research that interests me?
And so I began by writing a curriculum for myself.
Basics: Programming (Python), Statistics, Algorithms, Epidemiology
Advanced: Introduction to data
science, Visualization, Machine learning
The problem with the 7-8 months scheduled (with no degree) is keeping motivated.
A blog is great for keeping motivated.Here I plan to post updates on my courses, and perhaps review articles that interest me. I am going to use the pomodoro technique, so will write actual time spent.
As for the curriculum: I have a bit of programming in my background (Matlab, Python) but don't feel strong enough. I also am realistic that courses will cost money. I decided to audit the intro courses, and pay for advanced courses. This way I will get to learn and review at the beginning, and get certificates for more advanced courses.
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