Minding the Borderlands

Mark Koester (@markwkoester) on the art of travel and technology

How to Track Your Mobility: Training for Performance and Injury Prevention

Your body is a machine you should know how to manage. For a racecar driver or motorbike racer, success or failure relies on keeping their machine in good working order and improving it. Your body is no different. You need to know how to access it, maintain it, and optimize it.

This is one of the interesting “unexpected” lessons I gained since I started running about two years ago. I played sports growing up, but looking back I never really trained at any previous phrase in my life. While in the end I reached several high points through my run training, including two full marathons and two half-marathons, interestingly I feel like my biggest learning was about my body and ongoing maintenance and respect you need to treat it with.

One of the key aspects of bodily maintenance and improvement is mobility. As author and proponent of mobility training Kelly Starrett likes to say, “All human beings should be able to perform basic maintenance on themselves.”

What is mobility? Mobility are exercise practices to ensure you are ready to perform correctly in your sport or physical activity. It means both being “ready” such that you won’t hurt yourself but also “ready” such that you can perform at your best. We might summarize mobility as better movement patterns, a stronger body to hold those positions and massage techniques to release tensions.

Mobility is a popular topic these days in nearly all areas of fitness, sports and performance. For running, in particular, mobility is touted as one of the key factors to running injury-free, which includes mobility, stability (i.e. strength) and deep tissue release.

You shouldn’t think about mobility as limited to “pre-hab,” meaning exercises to prevent injury. In fact, improved movement patterns and better stability strength can improve your performance. This means bigger gains in your lifts at the gyms and faster times at your races. Improved mobility translates to less mental strain since you become a well-oiled machine. Personally I’ve seen how important mobility work can be to allowing me to train hard and perform at my best.

Like running itself (and a lot of other aspects of my life), mobility is something I track and measure. I believe all goals, especially those in health and fitness, should be tracked. (SEE: Why Track Your Workouts?) You should be tracking your goals from two sides: Are you putting in the the necessary time for that pursuit? (commitment time tracking) and Are you making improvements in that pursuit? (progress tracking). In the case of mobility, you can track your mobility through assessments (i.e. exercises to gauge where you are and your progress) and through logging the time you send on your mobility workouts themselves. For me that’s the essence of being data-driven: tracking so you can reach a goal more effectively.

In this post, we are going to look at mobility training. We start by defining the importance of mobility for both injury prevention and improved performance. We will then look at a few tests to assess your mobility and, consider how expansive a topic it is, I’ll share a bunch of resources for further study. Finally, we will look at how to build a mobility routine and how to track it in terms of your weekly time commitment and in terms of your strengths and weaknesses.

Mood Tracking Apps on iOS: A Review of Apps for Logging Your Emotional Life

Here is my review of various apps and services you can use to track your mood on iOS or with your Apple Watch.

I wanted to try and track my mood. Since I’ve already tracked dozens of personal data points from my my health and my productivity to more obscure areas, I decided I want to explore one of the more active areas of self-quantification: mood tracking.

There are various psychological, philosophical and even practical challenges to measuring your moods. Firstly, one of the biggest problems is just now to define what is a mood? Secondarily, how best to “capture” and score that mood? I explored these questions and a few others in An Exploration of Mood Tracking: Can We Measure How We Feel?. My conclusion there was that moods are complex and most mood tracking is at best an abstraction. That said, for a self-tracker like me, mood tracking merited an experiment.

In this post, I want to share a review of the various mood tracking tools I tried, two of my favorites (MoodNotes and iMoodJournal) and my approach to mood tracking.

What Do Your Photos Say About You? Exploring Your Photo Metadata With PhotoStats App

475.

That is roughly the number of photos that was taken by each smart phone user in 2017. That’s a lot of photos. That’s a lot of data.

But with this photo data, what insights and learnings are we able to get from our photos?

The photos on your mobile phone are one of the richest collections of data you have on yourself. If you regularly take photos with your phone (which most of us do), then your phone is collecting data on you.

Or to put it a more positive way, when you take photos, you also collect some additional data like where you were, what time and certain conditions at the time of the photo taking event.

Unfortunately, we aren’t leveraging our photo data to understand ourselves and our behaviors. While photo backup services might use our photo data, most actually strip the metadata on our photos and we lose it forever.

Fortunately there is a solution to start tracking, recording and leveraging your photo’s metadata. Using a new app called PhotoStats.io, you can are able to backup your photo metadata on your phone and, in turn, start leveraging it to understand a piece of data about your life.

In this post, I want to explore, visualize and start to understand my photo data. First, I want to share how to collect your metadata on your photos. We will get all of the metadata on your phone’s photos using an app called PhotoStats. Second, we will look into the information we can glean off that data as well as create a few simple visualizations and infographics. We will conclude with a bit of initial data exploration and visualization and some further areas for future research.

An Exploration of Mood Tracking: Can We Measure How We Feel?

What is a mood? Can we track it? Can we improve it?

After a few previous failed attempts, I decided to try another exploration into mood tracking.

CONFESSION: I’m not depressed and not particularly prone to the “blues” either. I do have waxing and waning motivation and drive though. In fact, I tend to be a good mood most of the time and quite productive too.

Instead, I’m curious self-tracker, and for this mood tracking experiment, I wanted to see if I could better understand what affects my mood by tracking it. Hopefully, eventually, I can avoid factors that create negative moods and optimize my life to be in a better mood more often.

Unlike other tracking metrics like productivity, money or even the somewhat ambiguous idea of measuring good heath, mood isn’t easy to quantify. It’s difficult to be objective about our moods or even “score” our current mood. Scientists also struggle with measuring our moods for both practical and even philosophical reasons.

In this post, we will explore what is mood tracking and some of the problems in measuring it. We will take a look at the psychological understanding of moods and different ways scientists measure it. In the conclusion, I’ll share some of the problems I see in mood tracking.

NOTE: In future posts, I’ll share my review of a few mood tracking apps as well as how I tracked my mood and what I learned.

My Year in Podcast Listening: 2017

In 2017, I listened to 298 hours of podcasts. To put it in perspective, roughly 3.4% of my total time during last year went to podcast listening.

Compared to my 2016 podcast listening, I increased my daily podcast listening by 31 minutes per day (from 18 min per day in 2016 to 49 in 2017).

I mostly consume podcasts during the week (less on weekends). I listen to podcasts more often while traveling, and but I also tune in while running and during workouts.

How do I track my podcast listening? A bit over a year ago, I decided to “scratch my own itch,” and I built one of the first podcast tracking web services called PodcastTracker.com. It remains a simple service that helps self-trackers log what they listen to and export a log for visualization like I have done.

What did I learn? In this post, I want to share my year in podcast listening. For example, how much listening did I do? What were my favorite podcasts? What periods did I listen to podcasts? Finally I’ll conclude with a note about what I’ve learned.

Let’s first check out the full infographic.

2017 My Annual Review

As I look back on this past year and the past couple years before that, I can’t help but think to feel gratitude. Again and again good years pile up. Each year I tell me myself that was a best years ever.

I consider myself incredibly fortunate in my projects, life and pursuits. At the same time I also have taken risks and pursued passions that have allowed me to find ways to turn each year into something better, different and evolving than the last.

In all honesty, no year is perfect. There are plenty of up’s and down’s, moments of self-doubt, serious disappointment and plenty of uncertainty. You get sick. You miss out on something. You fail. That’s life.

In spite of these setbacks, I’m happy to report that 2017 was another great year for me. Several things didn’t pane out or went sideways, but I achieved nearly all of what I had hoped for this past year and many new positive surprises too. This includes good health and fitness, interesting travel and adventures, new things learned and tried, and lots building and writing too.

Using A Year in Numbers: My Data From 2017, here is my year in review.

A Year in Numbers: My Data From 2017

2017 was a year of self-tracking and personal data. So inspired by all the data I collected and some new found abilities in data visualizations, here is my year in data

(NOTE: Lessons learned from all of this tracking will be covered in separate post. My formal annual review will also be in a unique post.)

Let’s look at my past year… in data.

My Year in Book Reading: 2017

2017 was another great year of reading for me.

Numbers: I read 21,687 pages across 62 books and across 14 different genres. In terms of novels, I read mostly science-fiction, but I also added a few classics too. My non-fiction reading spanned a number of topics with a slight focus on science, business, technology, and history. My biggest month of reading was September 2017 in which I finished 10 books and 3672 pages.

One other topical reading highlight for 2017 was health and fitness, especially long-distance running. Along with completing two marathons and two half marathons, I read several great books on running and you have view my ratings and review in Science and Stories of Running and Some Great Books on Running.

Below is my data visualization of my year in reading, the top 5 books I read, and a few conclusions from this year and year to come.

Running Your Best: A Comparison of Race Predictors, Calculators and Models

Running your best race requires a number of good things to happen. You need to have done solid training and gotten yourself as ready as possible. You need to be healthy and well-rested. You need to know the conditions and have the necessary gear for the race. But arguably one of the most important factors in running your best is having a good prediction.

There are a number of tools and methods online to to estimate and predict your next race time. These race predictor calculators looks at factors like weekly running distance and previous time at a short distance (like the half marathon or 5k) to guess your expected next race time.

While there is a segment of runners who just want to finish and don’t care much about performance or improvement, I don’t really include myself in that group. Like the vast majority of runners I know, we are trying to run our best at races. We want hit a target and get a Personal Best (PBs).

Later this week I’ll be running my second marathon in Chiang Mai, Thailand. I’m happy to say that my training has gone quite well. I’ve been using an smart, adaptive training program from TrainAsOne in order to push my different capacities, avoid injury and get myself ready. At the time of writing, my conditions are good. I’m not sick, and I’ve been getting good sleep and decent nutrition.

In this post, I want to look at several of the predictors, calculators and models used to estimate run times. We will be using my recent half marathon score (1:45:00) and training log to see what these models predict from my next race. Unfortunately, there are a lot of models and predictors out there and there is some degree of variance. Several of the older models were purely mathematical and their predictions are dangerously optimistic. More recent models are based on statistical data and algorithms to make their predictions.