Aggregate Monthly Timeline

The Timeline in the Crickles Navigator shows your Cardiac Stress for each activity in the selected Date range. There is now a new feature that enables you to see your aggregate Cardiac Stress month by month. To select this visualisation, when you’re on the Timeline tab check the Aggregate monthly timeline? checkbox in the side panel. Then you’ll see a chart like this:

Monthly_Timeline

Each point on the chart now represents a whole month rather than a single activity. Also, the x-axis here doesn’t depend on the Date range but reflects the full extent of your history loaded into Crickles. The first point of every year – the one that sits exactly on the vertical year line (e.g. the first point in the figure, which lies on the 2015 line) corresponds to January and the last point, which sits before the start of the next year, shows the December value.

The points are still coloured by Cardiac Intensity and sized by Hours of moving time, as on the normal Timeline. However, since aggregating by month tends to even out differences, the size scaling is emphasised on the Monthly display.

This new chart gives you the ability to identify your easier and harder months, and to see at a glance how the Cardiac Stress accrued in a particular month arose from duration   versus intensity of exercise. For example, in the figure you can see that after a month of very low Cardiac Stress in October 2016, the two months following saw a return to higher levels, generated by greater intensity – this was due to the increased difficulty of achieving previously normal performance after a period of de-training.

Obviously the last point on the chart will normally reflect the current month so Hours (indicated by size) and also the level of Cardiac Stress will tend to be lower than normal until the month comes to an end. There is no correction for shorter/longer months.

To compare your results with others, use the Compare Groups? checkbox. This brings the Group dropdown into play, giving you the ability to compare your monthly Cardiac Stress to all other Crickles athletes, or to sub-select based on your gender or age:

Monthly_t_group

The larger the group you compare to the more Intensity will tend towards mid orange and the less variability there will be in Hours. You will also notice a seasonal trend with a high in Summer and a low in Winter (so long as the Crickles cohort remains heavily weighted towards the northern hemisphere!).

Michael Goolaerts

I was going to write about something else this weekend. But then I read that Michael Goolaerts had a cardiac arrest at Paris-Roubaix.

He reportedly had a cardiac arrest after a crash and received CPR at the roadside.

We don’t know much more at the time of writing.

He may have simply crashed and struck his chest. He was on the cobbles at the time, and they are notorious. Commotio Cordis is the term given when a blow to the chest wall put the heart into ventricular fibrillation. It’s most common in boys/young men when they are playing sport. It’s more common than you might think. According to Wikipedia there were 188 cases in the US between 1996 and 2007. 4 in 5 died.

There are a few conditions which he might have had. I thought that cycling at this level would require a basic history, exam, electrocardiogram (which looks at the electrical system) and echocardiogram (echo). An echo is not necessary it would seem (see here). Echocardiography looks at the structure of the heart and is a very common test that is requested on most patients a cardiologist would see. If I was competing at such a level (which I never will) I would want one. Therefore the medical exam has to be considered as pretty cursory.

Nonetheless, the screening as it is might be expected to exclude conditions such as hypertrophic cardiomyopathy (the muscle layers of the heart become disorganised), possibly arrhythmogenic right ventricular dysplasia (fat replaces muscle in the heart), and significant valve disease such as aortic stenosis (blood can’t get out of the heart properly). These are structural problems with the heart which can result in sudden death. Problems with the electrical system such as long QT syndrome or Brugada syndrome (these can predispose people to abnormal life threatening heart rhythm problems) could also be picked up.

Aside from an echo, an exercise ECG might help too – that is an ECG recorded during (and shortly after) exercise. One of the most common causes of death during exercise in young people are aberrant coronary arteries. The coronary arteries supply the heart with blood. Sometimes they take odd paths through the body. There is one route which they sometimes take when one of the runs between the aorta (the tube that takes blood from the heart around the body) and the pulmonary artery (the tube that takes blood from the heart to the lungs). At times of physical stress the coronary artery can become compressed and blood can’t get to the heart, resulting in cardiac arrest. You can see the early stages of that on an exercise test.

There is always the spectre of drug use too. I hate to say it, and I hate to think it, but I am still suspicious of what goes on in professional cycling (and other sports).

Sadly the outlook from “out-of-hospital”, and indeed “in-hospital” cardiac arrest is still poor. But I hope he will be ok. He will have been spotted quickly and had good cardiopulmonary resuscitation early, which will improve his chances. He’s also young and fit, which again stands in his favour. Fingers crossed.

Mark

Closest in Age

To view results on the Crickles Navigator you are now required to provide your Date of Birth. This enables us to run heart rate analysis based upon age and is needed for some of the features that we plan to introduce.

More immediately, it gives you the ability to compare your Navigator analytics with those of people who are close in age. To do this, simply pick Closest in age from the Group dropdown. This then compares your results to those of the athletes who are are closest in age to you:

Closest in age

The number of athletes who appear in light blue will depend upon the number who have signed up and disclosed their dates of birth at the time (as well, of course, upon the selection algorithm, which may change). The label in the top left of the chart shows the percentage of the Crickles population that are included in the Group selection. This now appears for all Group choices apart from All.

The Closest in age comparison applies to all of the tabs for which the Group choice is generally relevant. At the time of writing this is the first four, viz: Relative CSS, Relative profile, All-in and Summary. For each of these you can now compare your values with those of age-similar peers on Crickles.

For example, on the Summary tab when I select Closest in age I can see that my Current LTHR is at the 81st percent for my age group, whereas it’s only at the 59th percentile for the overall Crickles population (not illustrated here but can be seen by setting Group to All):

Closest in Age Summary

The identity of the athletes who are picked out as close in age is not disclosed on any o the tabs, nor is your identity disclosed to others when you appear as a comparison for them.

Change to the Fit-Fat charts

The Planner introduced a version of the Fit-Fat charts that, rather than looking backwards, projects forward into the future. Since its purpose is to assist with fitness planning, the Planner introduces High Fatigue warning lines to show when a proposed training load would push the athlete to a level of Fatigue that would be high for the athlete and/or high for the entire Crickles cohort.

This presentation of Fitfat charts is now available on Fit-Fat itself. Here’s an example with a Date range from 1/Nov/2017 to 7/Apr/2018:

ff_newform

This works well with short to medium Date ranges. Over a longer Date range the Fatigue, Form and Fitness lines can become unhelpfully overlapping. Also, the High Fatigue lines become somewhat redundant: in the limit, if you sample all of your available data by pushing the start of the Data range back to 1/Jan/2015 you get a very good view of how your current Fatigue compares to its historical levels without the need for the warning lines. Accordingly, for longer Date ranges the Fit-Fat display reverts back to its previous form. The cross-over occurs at around six months. In the example above, we can see this happen if the Date range is nudged one month further back:

ff_oldform

Over this time horizon of around six months there’s not much to choose between the two views. The more stripped down format works better as the length of the Date range increases and the new format taken from the Planner works better over shorter horizons at which you lose the long-term picture.

46:31

I have been in the 48:30s for my 10Ks so far. Mo Farah can sleep soundly in his bed. My fastest 10K, according to Strava, came during the Bristol Half Marathon last year (surprisingly). On Tuesday night, in my Nike’s I ran 46:31. About 2 minutes faster. So the shoes are better in the real world too. Frustratingly I can’t remember my previous best – and Strava doesn’t give you a list for some unknown reason.

Looking back at some of the segments I have run repeatedly during the route, I was a few percent faster consistently. What really surprised me was that I had done a run in the morning, and an hour and a half on the turbo too, doing intervals, so I was far from rested.

The shoes are not going back. I’m amazed. If you buy a TT bike and a speed suit and a helmet, and spend time in the wind tunnel, you expect some time back “for free”. But I had never expected that shoes could make so much difference to running.

 

Fitness planning with Crickles – UPDATED

UPDATES

What is written below correctly describes the Planner functionality but there are a couple of new updates since this was written, namely:

  1. Forecast method. For athletes for whom Crickles has uploaded at least a year of data there are two new Forecast methods for projecting your exercise load. The method described in the post below is now called Average based on Date range. The first new method is called Trend. This takes your average CSS per day from exactly one year before the forecast period (which is from now to your chosen Target date) and scales this up or down according to your current v. last year prior exercise load. The advantage of this over the Average method is that it accounts for both seasonality and year-to-year trend, which are typically very significant factors. Like the Average method, this generates ‘per day of week’ exercise load that you can adjust to suit your training programme. The second new method is called Repeat block from the past. When you choose this you’re prompted to enter a Start date to repeat from. Given this, the actual exercise loads that you incurred from that start date are projected forward from now to your Target date. For this method the adjustable per day of week levels do not appear. This enables you to see exactly how your Fitness, Fatigue and Form will evolve between now and race day if you repeat a training block from the past.
  2. Taper periods. The default taper periods described below were taken from analysis of the tapers of successful marathon runners prior to a marathon. These allow for a substantial amount of recovery and lead to very high Form levels on race day at a cost to Fitness (which is the point of tapering). These defaults have been changed to levels that are recommended for (and typical of cyclists) – a two week instead of a three week taper and of more modest extent. Of course, you can freely set the Taper levels as you see fit and this only affects the defaults.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The Crickles Fit-Fat tab shows you how your Fitness, Fatigue and Form have evolved based upon the Crickles model for Cardiac Stress. Now, you can also use the new Crickles Fitness Planner to anticipate how Fitness, Fatigue and Form will respond to a training programme geared towards an event. This is accessed on the Planner tab.

Entering the data

When you first visit the Planner you’ll see that the sidebar changes to the following:

planner_sidebar

Target date defaults to today + 3 months. You should change it according to your fitness planning horizon – say the date of your next significant athletic event. (It won’t accept a date less than one month into the future.)

The Sundays to Saturdays fields in the CSS Regime section are populated with your average CSS (actually your ‘extended CSS’ or XSS) for each day of the week sampled over the Date range. In this screenshot, I’ve extended my Date range back to 01/01/2015 to get a longer-term sampling period. You should adjust these numbers to reflect your planned training programme over the period up to your event. It’s best to do this using typical observed CSS values from the Activities tab. For example, in my case my usual park loops on a Wednesday or a Saturday park run will each score me about 40 CSS points; an hour on the turbo will typically get me 80; and a weekend club ride or equivalent might score about 320. Of course, if I want to increase my Fitness I’ll have to do more than I’ve done in the past!

The three Taper period fields enable you to specify how you plan to step down your exercise in the three weeks leading up to the target date. The fields are pre-populated with typical percentages – e.g. a reduction to 70% of a normal training week in the third week before the target date – and you can change these if you wish.

As you change all of these values the charts will change accordingly.

What you should aim for

The general aim of a Fit-Fat-Form training programme can be summarised in three principles:

  1. Add training volume to increase Fitness
  2. Taper before your event to flush out Fatigue and hence increase Form
  3. Don’t take on so much exercise load that your Fatigue gets to unhealthy levels.

Of course, there are many other important factors too such as working on strength and flexibility, managing any medications you may be taking, and balancing non-exercise factors such as diet, workload, travel and sleep; these lie outside the scope of this tool.

There is no established science on correct or optimum levels for Fitness or Fatigue. However, you can see if your own Fitness is increasing, staying flat or falling. Regarding Fatigue, the charts show you two useful levels as horizontal dashed red lines:

  • What is a high Fatigue level for you. This is calculated from your 90th percentile of Fatigue over all of your data on Crickles.
  • What constitutes a high Fatigue level for all of the athletes on Crickles. This is calculated from the 90th percentile of the 90th percentiles of each Crickles athlete.

Each of these two levels is marked on the Planner chart. As a fact of statistics, most of us will find our own high Fatigue level lies at a smaller absolute value than the high Crickles Fatigue level. When you train hard and do tough sessions you will occasionally push yourself beyond your own high Fatigue level. However, when planning a training programme if you see that your Fatigue levels are sustained for several days at a greater negative level than your high level, and especially if they are also sustained beyond the Crickles high level, it would be prudent to build in more recovery days.

So with reference to the three aims of the Fit-Fat-Form programme given above, you want to see that:

  1. Your Fitness at the target date is higher than it is today
  2. Your Form at the target date is comfortably positive – ideally at levels of 25 or more for the days leading  up to your event
  3. At no point during the course of the programme is your Fatigue hanging for days on end below the high lines, and especially not below both lines.

Example

The longer you give yourself to train, the easier it will be to meet all three of these training goals. Over three months it is certainly possible, and over a longer period it is easier. To take my numbers given on the input panel above as an example, if I project forwards based on no change except for a taper period I’ll find that my Fitness at the target date is lower than my Fitness now, while my Form will be much higher/better: both effects are simple consequences of the taper period. However, if I increase my average exercise load on Sundays from 131 to 250 while leaving all other values the same I’ll get this:

planner_chart

My Fitness increases by about 10%, my Form is nicely high leading up to the target date and although my Fatigue sometimes dips below my own “high level”, it never stays there for long; moreover, my Fatigue never falls below the “Crickles high fatigue” level set by our hardiest athletes.

A final thought

There are limitations to any model and, as I’ve noted, there are important health and fitness factors that are not addressed at all by this kind of model. However, the Crickles methodology for assessing cardiac stress is robust and can be applied consistently to any form of exercise (although the estimate quality is reduced when heart rate is not captured). Furthermore, if you set a CSS-based exercise programme using this approach and stick to it then – as a mathematical fact and not just with the vagueness of an estimate – your Fitness, Form and Fatigue on the target date will be exactly as they were projected to be at the start of the programme!

 

Can shoes make you faster? It seems they can – on a treadmill at least.

Last May I was struggling with lateral knee pain. For days after the Taunton half marathon I couldn’t walk down stairs. I had tried a few things and was about to spend a lot on a trip to a podiatrist. Instead I went to TriUK in Yeovil and had a go at their “Mizuno Running Solution”. They picked out some shoes for me and I haven’t looked back – I haven’t had knee pain since (I have had Achilles discomfort, lateral foot pain and posterior tibial tendonitis mind you – but nothing terrible). I’m on my third pair of Mizuno’s now. My wife and kids have pairs too.

I have been using the Wave Mujin 3 shoes over the winter. They have taken me through umpteen muddy fields and down paths as well as covered a lot of road miles. I have probably done 400-500Km in them. They have been through the washing machine umpteen times (I know…).

I thought it was time for some new shoes. The Nike app texted me last week to say that their new lightweight Epic React Flyknits were available. 5 minutes later I had ordered a pair and they arrived last Wednesday. They have a new type of foam in (“React”). It’s durable and returns energy to the runner. It’s no Zoom Vaporfly 4%, with no carbon fibre plate (as far as I know). But it’s not exorbitantly expensive and the shoes expected to be durable – 600 miles or so.

On Friday I took them for a spin. On a treadmill. My “fast” mile felt easier than ever – rather than feeling as though I was going to pass out at the end of it, I was increasing the speed and went on for 2Km.

Psychological? Possibly. I firmly believe in psychology. I have a degree in it, and my PhD was on “central fatigue” – the concept that your brain limits your exercise capacity. I spent a lot of time doing transcranial magnetic stimulation. It’s odd to see the resurgence recently in brain stimulation. There is no doubt that new kit gives you a boost. But certain parameters, such as heart rate don’t (shouldn’t) lie.

Last night I settled down for some science. I’ll need to replicate this again, probably next Sunday, but the bottom line is that the new shoes seem faster.

Some methods. I warmed up outside with a 3K run up and down our local hill. I then did 5 intervals on our treadmill: 800m at 11.3Km/h then 800m at 16.2Km/h then a brief rest (I let the treadmill run at 9Km/h for 400m – 160s). A total of 8K. That rest time allowed me to slow down, get off the treadmill and change my shoes and my footpod (Polar). I used a Polar H10 heart rate strap and recorded it on my Garmin 935. The first interval was with the Mizuno shoes. Then two intervals with the Nike’s. Then an interval with the Mizuno’s and then a final interval with the Nike’s. For reference I weighed the shoes – a single Nike weighs 234g, a single Mizuno weighs 388g – an important difference.

Note I can run “faster” on the treadmill than in real life. I have just managed a Sub 20 5K on the treadmill, but it’s still just over (by about 40s) on the local 5K park run. 11.3Km/h is the speed I target for endurance. Last year that kept my heart rate about 130-135bpm. 16.2Km/h is the fastest I can sustain for a mile. Because I wanted to see if the new shoes were faster I didn’t want to hit my maximum heart rate and plateau, so although I can go a (little) bit faster over 800m I kept it at this level.

What did it show?

Firstly, it felt way easier with the Nike’s. There is a definite squish and you can feel the shoe deforming on impact with your toes and forefoot sinking into the shoe. You don’t get that with the Mizunos. But was it physiologically easier?

It looks like it was. See figure 1. It has two traces on it. There is an actual speed (according to the treadmill) line to demonstrate the intervals and a heart rate trace. The orange intervals are when I have the Mizuno’s on. The Black intervals are when I have the Nike’s on.

Figure 1.

Nike vs. Mizuno

Firstly, from left to right there is a gradual increase in heart rate. For me, running the 5 back to back intervals was hard. I’m also in a small, hot stuffy room. So I get tired and dehydrated.

But the interesting thing for me is that my peak heart rate is higher on interval 4 than interval 5, by just over 4 beats per minute. The trend would suggest it should be the other way around. Not much – but around 3%.

The really interesting thing is the speed data from the footpod though. The next chart (figure 2) shows the actual speed (calibrated – the actual data suggested I was much faster than I was!). As far as the footpod was concerned I was running more slowly (about 2Km/h) with the Nike’s on compared with the Mizuno’s, although the treadmill speed was the same.

Figure 2.

N Vs. M 3

I’m not quite sure why this is. Running in the Nike’s changed my technique though. I felt a bit more tipped forward and a bit more on my toes. I also noticed a bounce, and I would imagine I spent more time in the air, allowing the treadmill to travel further beneath me.

I have summarised the data in the table (table 1). I can go faster on the treadmill in the Nike’s. But am I faster in real life? I’ll try to answer that soon. As soon as the weather dries up anyway and I have some time to do some intervals outside. I don’t want to get my Nike’s dirty yet, and time is a bit tight at the moment. I also need to do a long run first to see if they are comfortable enough. The toe box is a bit small, and I don’t know how my knee will stand up to things. If I can’t get on with them, back they will go. Despite the treadmill times.

Table 1.

Interval Shoe

Treadmill Speed

Footpod Speed Mean Heart Rate

Max Heart Rate

Interval 1 Mizuno

11.3

11.2 117

123

  Mizuno

16.2

18.0 144

152

Interval 2 Nike

11.3

11.0 118

125

  Nike

16.2

15.0 143

150

Interval 3 Nike

11.3

11.0 119

126

  Nike

16.2

14.8 142

148

Interval 4 Mizuno

11.3

10.9 124

130

  Mizuno

16.2

17.3 149

155

Interval 5 Nike

11.3

10.8 123

128

  Nike 16.2 15.1 146 151
Interval 1+4 Mizuno

11.3

11.1 120

127

  Mizuno

16.2

17.6 147

154

Interval 2+3+5 Nike

11.3

10.9 120

126

  Nike

16.2

15.0 144

150

Quakinghouse Lane

This morning I ran up Quakinghouse lane with the dogs and one of my kids. For about the first time in a while I enjoyed it. At the top of the hill the sun was rising, the wind had dropped, and I could see out over the vale of Taunton. There was a chance I would get home with warm, dry feet.

It’s been a long hard winter, and running up and down that hill hasn’t been pleasurable. But people who run (or cycle) are addicted to running (or cycling), and I am now a slave to it. And it is an addiction.

There is a long-running study looking at Ultra marathoners (the ULTRA study). It takes a little finding. There is a study on the treatment of fibroids known as Ultra, and project MKUltra is also known as the CIA mind control program – allegedly.

The signup for the study is here if you are interested. It’s an ambitious project to determine whether running long distances is healthy. If you are one of the many who has done an Ultra, then go and sign up.

But the interesting thing about people who do Ultras (people can enrol in the study if they have run an event of more than 50K) is that they don’t care if it’s healthy or not. Participants in the study were asked to answer “yes” or “no” to the question “If you were to learn, with absolute certainty, that ultramarathon running is bad for your health, would you stop your ultramarathon training and participation?” Of 1349 who answered the question, almost ¾ answered “no”. My guess is that this would hold true for cyclists and triathletes too. The Pubmed link is here if you want to read a bit more.

I see people with heart trouble every day of the week, that may be due to exercise. I haven’t stopped yet and won’t for as long as my health and my joints can keep going.

It absolutely p****d down as I ran down Quakinghouse lane. The shoes are on the Aga. I’ll be out again tomorrow.

Strava have lost Friends

Recently Strava’s API for getting Friends has stopped working. This hasn’t only affected Crickles: numerous other app developers are reporting the same problem. There is no information about this on the Strava developers site, which still reports the same protocol for accessing Friends data, so there is no way of knowing if it’s an unintended bug or a planned removal of functionality. Strava are not replying to Support queries on the topic.

This means that any changes to your Strava Friends made after January 17th will not be reflected in Crickles when you select Strava Friends under Group. If you signed up to Crickles after January 17th your Strava Friends will only include you!

It remains to be seen whether or not Strava will fix this. Irrespective, this is a good prompt for Crickles to start to collect athletes’ Date of Birth so that,  in lieu of Friends if this is not available, we can do Age as well as Gender grouping. Age is in fact more relevant than Friends from the perspective of health and training load.

Those of you who asked for beta logins have already provided your DoB; everyone else can expect to be prompted to supply it soon. Your identity will not be shown with your age on Crickles, even though it is on Strava. It would be useful to hear from anyone who prefers not to provide their DoB – please email me at ian@crickles.org if you feel this way.

Whether/how Friends-style comparisons will be supported if Strava don’t fix their API remains to be seen. Please let me know if you have views on its desirability.

Thanks, Ian

I don’t know what to call this one.

It’s been one of those weekends which haven’t been full of joy. At one stage yesterday, with the rain pouring and the wind blowing, I asked myself what I was doing out running up a muddy hill with the dogs. The answer was of course to take my mind of the Bawa-Garba case.

For those of you who don’t know, Dr Bawa-Garba was found guilty of manslaughter and struck off by the GMC after the tragic death of a child under her care. She made a mistake – that is clear – and it had terrible consequences. But there were, as there usually are with mistakes, also problems with the system in which she worked. She had just returned from maternity leave, was working in an unfamiliar environment, the IT system was playing up and she had with junior doctors supporting her with little experience. It has been reported that she was covering for absent colleagues, including the consultant as well. Is it any wonder that a mistake happened?

This winter in the NHS has left many of us rushing around, managing a much higher number of patients than usual, and consequently making hurried judgements. But what is the alternative – to say you will only see a certain number of patients, or down tools if members of the team are off with flu? The case has made many of us feel vulnerable. All doctors make errors at times. We know we make more errors when under pressure.

The practice of medicine relies on judgement and intuition – which is why it is error prone. It is not an exact science, and never will be.

It is that very lack of hard data that inspired Crickles. I was asked “how much exercise is too much”, and I couldn’t give an exact answer. Crickles draws on the wisdom of the crowds. You can look at your exercise volume and see where you are relative to others. You can look at your fitness and fatigue levels. As of this evening, I am fatigued according to my data. So, it’s a day off training tomorrow, thank God, as the weather looks crap.