a week of walking

This week, I focused on my walking habits. During my walks I try to take a break from technology and reflect on my day.

I generally work from 09:00 – 18:30 and try to get two 1-1.5 mile walks each day: one in the morning (~11:00) and the other in the afternoon (~15:00). In addition to the data that I collected, I used Google Fit to track my distance, steps, and “heart points”.


Google Fit: This data was recorded automatically from my phone; I do not use a wearable device. The data collected from the app is for all walking activity for the day – not just reserved walking time.

Personal: During my walks I recorded what my thoughts were primarily about – fleeting thoughts were not recorded. After each walk, I noted if I had been listening to music while I was walking.

Both of these data are displayed below:

a week of walking

Visualization Design

Google Fit displays data using concentric circles where the outer circle, green, represents “heart points” and the inner circle, blue, represents total steps – both of these are relative to set daily goas. I decided to emulate the circle style by breaking down my thoughts relative to the total walking time. I estimated the percentage of walking time by landmarks along my walking path. Other events, such as a phone notification, changed what I was thinking about these are marked by a single or double dash. The circles should be read counterclockwise.


I have used Google Fit for a while, but I haven’t looked at the data too closely; usually I just look at my daily step count. It was interesting to look over the daily breakdown and see how the app classifies some of my activity and how the app assigns “heart points”. Some walking activities with similar distance and “movement time” gave me vastly different heart points. Some entries of the exact same walk resulted in different distances yet similar steps (differing a few 10s of steps). The walking paths displayed by the app are also a bit wonky and that prompted a comparison to my tracking data from Google Maps. There, I saw some of my walking entries in Google Fit were marked as “Missing Activity”, and Maps displayed a more accurate walking path. Maps was also better at filtering out miscellaneous walking entries since Maps filters by location rather than moments of increased activity (e.g. bringing in the groceries).

Also looking over the data from Google Fit also gave me a chance to update my daily goals which were set before working remotely where I rarely sat at my desk leading to higher goals. This is a reminder that goals need to be continually re-evaluated and updated with changing environments.

During my walks I try to take a break from technology, and I would say that I have mostly accomplished that: 6/9 walks I did not listen to music and I did not respond to notifications half of the time. I also try to use the time to reflect on what I have been working on before or what I should do after the walk. For example, I usually take my afternoon walk around the time I finish lunch where I will catch up on reading or review the blog, and for most of the afternoon walks I spend some time thinking/reflecting on school work. Finally, there also seems to be a trend where a notification, especially if I respond, disrupts what I was thinking about. This makes sense since I would have had to stop what I was thinking about to respond which leads to new a train of thought.

1 thought on “a week of walking

  1. ‘During my walks I try to take a break from technology and reflect on my day’

    I think this is an important context for thinking about how we might ‘measure’ things in education. So much of learning analytics seems to be focused on ‘the act’ of participation, but maybe it is in the times and spaces away from formal educational activity that we undergo important part of our learning?

    I really like the visualisation here, and the circular shape conveys an interesting sense of ‘completeness’ – I suppose this suits your particular tracking quite well here because each walk was the same duration, and you are really just visualising how that time has been distributed. Emulating the ‘digital’ part of your tracking was a nice approach too. I did find myself wondering what the landmarks were!

    Good reflection here too, particularly about setting ‘goals’. This seems to give one agency, doesn’t it? Although, the kind of goal is predefined. I also wondered how this translates into education – clearly there are some aspects that benefit from students setting their own targets and goals, but there are other aspects that require institutions to define the end point, for example in summative assessments.

    Looks like you gains some useful insights from this tracking!

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