Garmin eTrex 30 review

I’ve been using the Garmin eTrex 30 for navigation and to record my rides for four years now. I like it a lot, but I don’t think it’s the GPS for everyone. Here’s my review.

Design and appearance

Garmin eTrex 30 mounted on road bike handlebarsTo be brutally honest, the eTrex is a chunky lump to put on your handlebars. If it was writing a lonely hearts ad it might describe itself as “rugged”. The device sticks up a good 45mm from the handlebars. It’s about 100mm long and 55mm wide. It can’t even pronounce the word aerodynamic. The colour probably won’t match your bike.

The reason it is a bit plus-sized is to fit two AA batteries and a 44x35mm screen. The wider reason for the form-factor is that the eTrex 30 is not aimed at racers, time-trialists or triathletes. It’s intended for hikers, sailors and touring cyclists. It is also very popular with audaxers like me.

Around the edge are five small rubbery buttons: Menu, Up, Down, Back and Light/On-Off. These need a firm press which can take a couple of tries in winter when I sometimes wear ski gloves, but I’d prefer this to them being flimsy and getting pressed by accident.

The screen is not touch-sensitive. Instead, on the top is a 4-direction “joystick”. It can also be pushed in to select items. Selection in this way is a bit tricky and it is easy to “miss” when trying to push the stick in and ending up pushing it up or down or doing nothing. I find this the same whether I’m wearing gloves or not. However, I’m seldom in a rush when using this button and I only tend to need it two or three times a day. I find that as long as I’m patient and pay attention to where I’m going the button does the job nicely. If my smartphone had an interface like this I’d hate it, but it doesn’t bother me on the GPS.

Features and function

The etrex 30 can be used for a lot of other activities like hiking, sailing, etc. It has a load of features I’ve never used, like a “Man Overboard” button. I can’t tell you about those features, as I’ve only ever used it for cycling.

eTrex front with joystick and screen showing time, Trip odometer, elevation, speed, etc.

My “data” screen

I followed some very thorough etrex 20/30 setup advice and went for a simple system of two screens, one for a the map, one for the numbers – time, distance, average speed, battery level, etc. You can choose which fields you want and have them in various layouts. I switch between the two screens using the back button which is quick and easy.

The main thing I use this for is guessing when I might arrive at the next control. If my average speed has dropped but my elevation is high, then I can expect to gain some speed when I descend. Arguably maximum speed is more for entertainment than anything else. Total ascent can be useful when Everesting or chasing AAA points.

I also put two boxes like these at the top of my map screen – Overall Ave. and Distance. You can have four boxes, but it starts to obscure the map a bit. With the help of the up and down buttons on the top left edge of the device (see image above) the map can be zoomed in or out much further than you’re likely to want. I use 120m scale for towns and 200/300m for countryside. If you get lost you can also scroll across the map using the joystick, but this is a bit clumsy and slow to update.

You can buy or find additional online maps, but I found those that are built-in to be fine for the UK.

When I’ve planned a long ride I usually copy a GPX file onto the device so I have something to follow for navigation. You can use the “Follow route” feature which provides a thick pink line as well as some peak and valley icons which don’t seem very accurate to me. For simplicity I prefer to simply “Show on map” and select a colour that I find easy to see. I prefer dark blue or red (see below).

eTrex screen showing red route not quite following every bend of the road

When trackpoints are reduced, the route shortcuts some of the curves of the road. This shot shows the pointer mode rather than my info boxes.

This is where you have to be a bit careful on longer rides. There is a track point limit and if you go over it, the end of the route will be cut short. I discovered this halfway through a 300km ride to my alarm. Thankfully I also had a routesheet. I’m not sure exactly what the maximum number of track points is, but it’s definitely more than the 500 points that older devices had. I’ve since learnt several ways to get around this and I always use the “View map” option to check my routes are about the right length after I’ve copied them across. If I use a tool to reduce the number of track points the route often ends up slightly shorter  – say 98.6km instead of 100km due to the way the reduced route takes shortcuts across the bends in a road (see image). For routes longer than 200km I’d prefer to split the route into several sections.

I don’t get any warnings or beeps if I go off-route, but with an audax routesheet alongside I find navigation pretty easy.

Like most GPSs the eTrex 30 records the track you travel on for Strava or other ride-recording tools. What I found different to the Edge 500 was that once it’s set up it records all the time, no need to press start. You can save your track to another file or clear the current track, but it will keep recording. If you turn the device off or even change the batteries it will continue recording when you turn it back on. If you’ve moved while it was off it draws a straight line between the points. What this means is that you need to remember to clear the current track before you start a new ride. That was it doesn’t include your car/train/plane journey!

Note: If you’re concerned that your total ascent figure is as accurate (and large!) as possible when uploading from an eTrex to Strava, I’ve made some scripts to fix the way the altimeter data is read.

Other features

  • Takes 2xAA batteries which are available anyhere. Rechargeables work fine.
  • Good battery life – I’ve had Eneloops last well over 24 hours.
  • Can be powered (but not charged) via USB.
  • Mini-USB data connector.
  • Good battery life – I’ve had Eneloops last well over 24 hours.
  • Can display HR and cadence if attached, but doesn’t record them for Strava, etc.
  • Secure bike mount available and lanyard attachment point.
  • Reliable – never had a crash or loss of data in four years.

Conclusions

I previously used a Garmin Edge 500. The Edge 500’s navigation was very basic, consisting only of a wiggly line, no map and a buzz when you’re off course… or the GPS signal has failed. But most annoyingly the Edge 500 has a non-removable Lithium Ion battery that I could never get more than 12 hours out of. While charging on the go is theoretically possible with the right kind of cable, I always found that this reset my route. As I understand it, most of the Edge series (apart form the Edge Touring) is designed for training rides where you might want to record HR, cadence, power, etc, but not ideal for audax/touring.

When all I want is to record my ride and it’s less than an hour long, my phone is simpler. But on longer rides I like to conserve my phone’s battery in case of emergencies. I haven’t tried every GPS out there, but in spite of the user interface quirks already mentioned, I’m very happy with the eTrex 30 for touring and audax.

This is not a tour 400A photos

Why I’m riding “This is not a tour”

This weekend I’m riding the 400km on and off-road audax in the style and memory of Mike Hall. My motivation for this ride is similar to the reason I ride audaxes in general, but with the added variety of off-road sections. I’m interested in the question, “How much harder will that be?”. I met Mike only briefly, but I think this kind of event is what he would have wanted to inspire.

Long distance cycling is something I’ve got into over the past five years. Whenever I’ve mentioned one of my rides to friends I get bewildered responses ranging from admiration to horror. A lot of people ask if I’m doing it for charity.

“No” I say, “I’m doing it for… fun?”.

Yes, fun. I enjoy planning the route, deciding what clothing, lights and bike maintenance kit I should take. I enjoy the challenge of not knowing whether I can finish within the time limits. I enjoy the peace and solitude exploring deserted country lanes. I enjoy chatting with other riders. Sometimes I’m winding my way up a hill, sometimes I’m concentrating on a tricky descent. Sometimes I’m ambling along, sometimes I’m pushing to go as fast as I can. I enjoy the freedom of roaming and of self-sufficiency. I enjoy getting away from it all, relaxed but focused on the ride.

I’m not claiming that every journey is smooth and full of picture-postcard scenery. Things go wrong. Punctures happen, wrong turns happen, lights fail. Headwinds, achy legs and cold temperatures conspire against an easy ride. On most rides I’ll have a “low point” when I’m fed up, uncomfortable or hungry. Getting through that and whatever other challenges the ride may throw at me is part of the challenge and the reason I feel elated if I finish.

And I don’t always finish in time. If I always succeeded I’d wonder if I was limiting myself to easy challenges. Failure is a good way to learn, even though it hurts at the time.

I’m sure most of my bewildered friends take on similar challenges. Things which take unusual mental or physical effort, which take us away from the humdrum of everyday life. Things where success is not guaranteed, where temporary discomfort is tolerated to reach a goal. Everyone’s challenges are different, but we all need to be challenged.

Can you relate to that?

Commuting to Bristol in photos

I’ve spent a few years infrequently commuting to Bristol through the year. I’ve now left that job, so thought it would be interesting to share the photos taken on my usual routes.

Audax training plan

I’ve got some longer audaxes planned this year, so I thought I should actually have a training plan for once. I’ve avoided stating exactly what ride I’ll do on what day as I know life is likely to get in the way, but I still have some targets which I think are reasonable. Perhaps publishing it here will keep me honest!

Jan – Feb

  • 1 x interval session (outside or turbo) 30-60 mins per month.
  • 1 x 50km+ ride per week (could turbo)
  • 2 x 100km+ ride with 1000m+ climbing per month (could count as two of the 50km)
  • 400km and 5000m total per month

 

Mar – Apr

  • 2 x interval sessions (outside or turbo) 30-60 mins per month.
  • 1 x 50km+ rides per week (could turbo)
  • 2 x 100km+ ride (could count as one of the 50km)
  • 1 x 200km+ ride with 2500m+ climbing per month (could count as one of the 50km)
  • 600km and 7500m total per month

 

May – July

  • 2 x interval sessions (outside or turbo) 30-60 mins per month.
  • 1 x 100km+ rides per week (could turbo)
  • 2 x 200km+ rides with 2500m+ climbing per month (could count as the 100km)
  • 1 x 300km+ ride with 4000m+ climbing per month (could count as one of the 200km)
  • 900km and 12000m total per month.

Sleep: What am I trying to measure?

My last post about analysing my sleep data had plenty of caveats, but despite my caution I started to wonder whether I was taking an interest in the right variables.

I’m aiming to sleep better for health and to feel more alert during the day. My first thought was to find out what influences how many hours I sleep each night. This was a guesstimate of my hours of sleep based on roughly when I fell asleep and woke up, minus any trips to the bathroom or time spent starting at the ceiling in frustration. Then I’d compare this to various lifestyle measures like how much I’d eaten, exercise, screen time, etc to see what, if anything correlated with a long sleep. Despite buying a gadget to help measure it, I’m not sure I have a more accurate measure of sleep quality, so approximate time asleep is what I tried.

However, I’ve realised that there are several ways in which “Hours that night” as I call it might not be the most useful measure. For example, there are times when I can’t get a full night’s sleep no matter how well prepared my body is for it. Sometimes I have to get up early for work, to go on holiday or because I have an audax that starts at 6am. Occasionally my daughter is ill and will wake me up several times. These things are thankfully rare, but could skew the results. I could simply delete any results where my maximum possible sleep was less than six hours, but this leaves less extreme cases.

I also recorded the maximum possible hours I could get each night. In my spreadsheet I subtracted the “Hours that night” from this to get “Missed sleep”, thinking that would be a better measure. On the other hand, if I can only get three hours maximum and I miss none, is that really better than having a Saturday lie-in for up to nine hours, but only sleeping for eight, meaning missed sleep is one hour? Who knows how many hours I might have got if I’d tried to sleep for more than three hours?

So I tried working out some kind of scaling adjustment, so that “missing” one hour out of a possible nine gives a better score than missing one hour out of a possible seven. I could ignore anything over eight hours as most people are unlikely to sleep that long unless they’ve missed out on sleep the night before. But that makes a hard cut-off, which feels wrong.

So I’ve come up with a simple scaling algorithm which looks like this:-

def missed_sleep_scaled(row):
    useful_max = min(target_sleep, row['Max possible (hrs)'])
    if useful_max == float(0):
        # result is invalid.
        return -1
    max_expected_hours = min(target_sleep, row['Max possible (hrs)'])
    useful_missed_sleep = max_expected_hours - min(row['Hours that night'], target_sleep)
    if useful_missed_sleep <= hours_noise_threshold:
        useful_missed_reduced_noise = float(0)
    else:
        useful_missed_reduced_noise = useful_missed_sleep
    return float(10) * useful_missed_reduced_noise / useful_max

This “sleep score” correlates less strongly with “Max possible (hrs)” than “missed sleep” did (0.104 vs 0.198). That seems like a step in the right direction. I’m uncertain about whether I should tweak it until it doesn’t correlate with “Max possible (hrs)” at all.

Some sleep correlation data

You may have read my previous post that I’m trying to use data to work out why I’m sometimes not sleeping well and how I might sleep better. I’ve been doing that now for some 86 days and I’m excited enough to look at the data and see if anything interesting has shown up. Ideally I’d like a year’s worth of data to get reliable results, but I’m impatient.

You may be wondering why the title of today’s post is so undramatic, prosaic even. Well, I’m rather a newbie when it comes to data science and I don’t want to leap to conclusions from the first thing I try. As you’ll see from my GitHub project, all I’ve done so far is to read in the data and use Python Pandas to produce the correlation results. I then pasted this into a spreadsheet, sorted and highlighted some rows.

I used to think that correlation implied causation. Then I took a stats class. Now I don't. Sounds like the class helped. Well, maybe.

I’m also wary that correlation does not imply causation. But it does make for an interesting start.

With those caveats out of the way, this is what I’ve got so far.

Screenshot of spreadsheet showing potential influences on the "hours that night" variable.

Plain correlation from the first 86 days of data

The factor I’m hoping to maximise is “Hours that night” – how many hours I sleep on a night after all those potential influences have been measured. So I’m interested in things which might be positive or negative influences on that.

The top two I’ve put in grey, as I think they’re not very interesting, except to show that the correlation function seems to be working as expected.

  • “Max possible” is low when I have to get up very early, say to travel somewhere, so it’s always going to limit my sleep.
  • “Av hrs past 5 days” is a rolling average of “Hours that night” over the last 5 days. That I’m more likely to sleep if I’ve built up a huge sleep debt recently is unsurprising, but also confirms that the model seems reliable.
  • ZMA and FOS are two supplements I’ve been taking recently which are said to help with sleep, the ZMA particularly for those doing a lot of exercise. Evidence is limited and I’m not keen on trying every eccentric treatment “because you never know”, but they’re cheap and the side-effects are trivial. However, I’ve only been taking these for a couple of weeks, so I don’t think there’s enough data to say if they have helped me.

Eating

If I had guessed I would’ve expected “Evening meal finish” – the time at which I finish dinner to have had the greatest negative effect on my sleep as I often wake early feeling boated if I’ve eaten late. It does seem to be a negative factor, along with “Evening meal size (0-5)”. I’ll aim to eat earlier and keep recording results.

Alcohol

This wasn’t a significant factor for me. This is supposed to make you fall asleep later but wake up too early, losing sleep overall. Anecdotally, I have found to be true for me. However, I drink quite rarely and haven’t had more than four units a day in any of the last 86 days, so my stats so far may not say much about that.

Daylight, Sugar, Screen time, Fasting

I’m recording these as they’ve either been blamed for bad sleep or hailed as a helpful thing. They don’t seem to be a big deal for me. I may consider stopping recording them so I have more time/space for other data.

Worry, Excitement

It interesting that these have some negative effect on my sleep, but as they’re all day values, there’s probably not a huge amount I can do to control them.

Exercise

I am surprised and, if I’m honest, disappointed to see “Exercise (1-5)” as such an apparently bad influence on my sleep. Studies have suggested that exercise should have a positive effect on sleep, but that may depend on intensity.

For me, and exercise score of 1 indicates a day where I didn’t walk for more than 15 mins and did no other exercise, 2 a normal day where I cycle to/from the station, about ten minutes each way, 3 is a bike ride of up to 3 hours or a 20-min weights/callisthenics session, 4 is a 3-6 hour bike ride, 5 is reserved for the all-day and sometimes all-night rides I occasionally do.

Perhaps this isn’t enough data on what, for me, may be an important question. Questions I’d like to answer might include.

  • Is morning exercise better or worse for sleep than evening exercise?
  • Is moderate exercise better for sleep than either extreme?
  • Does taking ZMA (or something else) mitigate the apparently negative effects of exercise on sleep?

Finally, instead of simply “Hours that night” should I be measuring the sleep I got as a fraction of the “Max possible” sleep? That might account for strange circumstances where I was still cycling at 1am and inevitably scored a 5 for “Exercise”.

Conclusions

I shouldn’t be drawing any firm conclusions yet, I think. 86 days is not that much data and there are many confounding factors that could be influencing things. I have a lot of thinking, learning and tweaking to do.

I plan to keep recording the data, expand my exercise data to include AM/PM and separate short intense efforts from longer endurance ones.

Volunteering at Thirsk for LEL

I recently volunteered for a few days at the Thirsk Control for London Edinburgh London. I put up banners, sorted out chargers for riders GPSs and phones, found beds for people, served food, fixed bikes and marshalled people into the control. It was tiring, but with a great bunch of people to work with it was also good fun.

Here are a few of my photos.

Lego ginger cat

Siobhan’s cat

Light green bags with LONDON EDINBURGH LONDON 2017 written on them

The bag drop has landed

Pens, space blankets, banners, signs, torches, etc laid out on a desk

A wide variety of kit is needed to run the control

LEL 2017 banner on school gate

Banners to guide the riders in

James standing in front of two LEL banners

Proud of my handiwork!

USB chargers on table.

Lots of charging for GPS and phones

Metal barriers in the car park

Parking for hundreds of bikes

School gym with mattresses laid out

A few beds in the overflow hall

Large school sports hall with many mattresses ready

160 more beds in the main hall

Three audax riders arriving at Thirsk school

An early group arrive before dark on Sunday

Boy on mountain bike

Not an audaxer

Woman on shopping bike with basket.

Also not an audaxer, but it’s hard to tell from a distance, OK?

Volunteers in school corridor

Edwin and Kate doing sleep running

Bikes parked at night

Plenty of bikes making use of the parking facilities

Volunteers in canteen

James and James learning how to serve food – it’s harder than it looks.

Pasta, Rice, Curry, Meatballs in canteen

Ja, I vill have ze pasta wiz ze curry. OK…

Bikes parked in daylight

A fairly busy time.

Red velomobile

Wow

Bike on stand being serviced

James looks into another gearing problem.

Light blue frame and front derailleur

Stiff shifting to the big ring, gunk in the cable duct. Lubed up and it was good enough.

Trouble sleeping

For a couple of years I’ve had some trouble sleeping. I’m not sure how much of this is due simply to age or the disruption of having a small child. However, lately I find that even when my daughter sleeps soundly, I often find myself waking too early.

I’ve taken the usual medical advice and found little improvement. GPs seem unwilling to investigate after they’ve done the standard checks and decided there’s nothing seriously wrong with me. Frustrated, I decided to do some work on it myself. Part of my day job is data analysis and I figured that if I can record the right data I might be able to write some code to work out what is causing me to lose sleep. Or, as Mark Watney might say,

I’m gonna have to data science the shit outta this!

Thumbnail image of Google docs spreadsheet

Sleep data Google docs spreadsheet

I started a couple of months ago, simply with a spreadsheet recording rather subjectively, thirteen different aspects of my day which I thought might influence my sleep. Things I thought might make a difference to my sleep but are hard to pin down without recording data due to the many confounding factors. 

Things like exercise, evening meal size, evening screen time, daylight hours, worry etc. Where no simple measurement scale exists, I estimate a number from one to five. So my ten minute cycle to the station and back counts as 2/5 for exercise. Only a 100km+ ride gets a 5/5. As I said, it’s a bit subjective, but hopefully enough to be useful. There’s no way I’m going to carry a light meter round with me or start weighing my meals. As it is, recording the data takes barely two minutes a day. The following morning I record roughly how much sleep I think I got, in hours. I have set the spreadsheet to calculate the average over the last five days.

What might be a bit more accurate is a gadget I’ve acquired recently, an EMFit QS. This intends to measure both my sleep and heart rate throughout the night, producing all kinds of numbers describing how well I’ve slept and recovered from exercise. I’m not sure I’ve fully understood how to use it or whether I’ve got it set up right yet, but I’m adding some of the numbers to my spreadsheet in the hope it will give me some clues.

I haven’t yet decided on what techniques I’ll use to analyse this data and spot patterns, but I intend to start with a straightforward correlation matrix before moving on to something more sophisticated that can look at results from previous days in case for example, the exercise or food I ate two days ago could influence my sleep tonight.

I’ll share any code I write on my github account and explain what I’ve tried here.

Knock Ventoux 2017

Few bike routes truly deserved the overused term “epic”, but I think Andy Corless’s Knock Ventoux 300km audax is a contender.

I rode this in June 2017 and here are my photos.