2019년 11월 25일 (월) | 버전 2.2.0-upd6
Found this app and decided to test it. The first workout gave no results since, as Oliver said, only a warnng with the IQ logo appeared on the datafield and grapghs were empty. I contacted the developer which told me of a knonw issue with Android and floating point numbers, he suggested to install the app via Garmin Express. I investigated a bit and found out that it is not a bug with floating point numbers but an issue due to the way settings are saved u nder android (0.8 is saved with a point as decimal separator instead of a comma like 0,8) and this causes the app not to work. So, the solution under android is to set "gradient averaging factor at 1 or 0) or to use garmin express to save the app's settings having the attention to write 0,8 in the "gradient averaging factor". Hope this helps. As to the significance of the data returned by the datafield, I did just one session, and I am still trying to uderstand values. I am studying the abstracts pointed out by the author.
One question to the developer: standing heart rate. What does this mean? The description says: enter standing heart rate, ca. rest heart rate -12. This sounds confusing (circa rest heart rate minus 12). Could you please explain a bit? I left the default value, but would like to adjust it to my real case. Thanks
2019년 11월 25일 (월), tmatthey71
Thx for investigating! The standing heart rate is about rest HR + 12, it should represent the HR at zero pace for running.
2018년 7월 3일 (화) | 버전 2.2.0-upd6
To those who are confused by this, the concept is actually pretty simple.
How do I know if I am getting better and by how much? Well, I can run the same distance and hopefully my time improves, or I can run a certain speed and see whether I can go further etc.... but all of these require me to train to exhaustion.
Instead think of your heart as an engine. There is a (mostly) linear relationship between your engine's RPM (heart BPM) and speed. So if a draw a graph of speed vs heart rate, it will slope up and show that I can do 15kmph at 180bpm.
Over time, as I adapt, I should find it easier to maintain that speed - ie maybe I can do 15kmph at 178bpm 176bpm etc. This means that the gradient of the line is changing - it gets less steep, because I am getting more speed out of each additional bpm. This means the "HR Index" decreases.
The cool thing is you can measure the index regardless of your speed. i.e. you don't need to keep running the same speed. Instead you measure the _relationship_ between speed and heart-rate.
The only thing I am unsure of is whether this should only really be used below anaerobic threshold - because if memory serves me correctly, the relationship is only linear _below_ the anaerobic threshold. Maybe the developer can confirm?
2018년 10월 2일 (화), tmatthey71
Your are correct. As soon you get into the anaerobic area the HR vs speed is not linear anymore. This finding was used by Conconi to find the aerobic - anaerobic threshold.
2018년 1월 20일 (토) | 버전 2.2.0
Doesnt work on Vivoactive 3. Datafield only shows the IQ Logo und the Graphs are emty.
Habe testet St least with 10 runs and from Firmware 2.60 to 2.90.
2018년 3월 5일 (월), tmatthey71
Thank you for the feedback. I'll upgrade to SDK 2.4.3 and test with the simulator with Vivoactive 3 and see if I get any errors.
2017년 11월 4일 (토) | 버전 188.8.131.52-upd1
The app does what it says and the integration with Connect via the .FIT file is impressive. The snag though is that at present it is simply a number with no context at all, I have tried reading the abstract of the academic paper and this suffers from the same issue of presuming that the reader understands the underlying assumptions. It would be very helpful in plain English to explain what the calculation is and what an index score going up or down actually means. For example I have scores in a range from 24.50 to 30.31 - is one value "better" than the other and if so which one and why? Until this is explained then it is intriguing but of no value.
2017년 11월 4일 (토), tmatthey71
The index reflexes the pace independent running performance; where a decrease means a performance increase. Optionally, the additional elevation effort can be include into the index equation to compensate for the pace reduction, or deduction when running downhill.
As already found by Conconi, there is a linear correlation of HR and running speed below aerobe - anaerobe threshold (https://de.wikipedia.org/wiki/Conconi-Test#/media/File:Conconi-Diagr.jpg). The calculate index is the linear factor of the linear curve HR vs pace. The index is an individual factor as standing HR and aerobe - anaerobe threshold are individual for each person. As along the standing HR is not changed, a lower index means improve performance; you can run at same pace, but with lower HR.
2017년 8월 8일 (화) | 버전 184.108.40.206-upd
The app works fine, no issues with that. Just, there is nothing that really explains what the numbers mean, how to use the numbers. Are you able to provide a link to explain what the numbers means. Thanks
2017년 8월 10일 (목), tmatthey71
The index shall be a pace independent HRB endurance number based on https://www.ncbi.nlm.nih.gov/pubmed/24345970:
The practical implications from the present study are that athletes and coaches can be confident in monitoring changes in endurance performance during training in the novel method. HR-RS index provides daily information about the adaptation to endurance training from different kind of running exercises without need to repeat laboratory tests during training. HR-RS index does require a baseline maximal field test (e.g. 3000m, cooper test) for determining HRmax and running speed corresponding to VO2max but after that normal running training with training information collections (running speed, HR) is adequate for monitoring changes in endurance performance during training. With the help of modern technology, like heart rate monitors and smartphones with GPS systems, it is able to get easily required information from every continuous-type running exercises for determining HR-RS index. The novel method is more practical and it provides daily information on adaptation to training for athletes and coaches compared to impractical and expensive maximal laboratory tests, which provide usually information on the adaptation a few times per year. Further, HR-RS index enables faster changes in training programs if training has led undesirable outcomes and help to achieve better adaptation to endurance training.
2017년 3월 27일 (월) | 버전 1.4.2
Difficult to learn a new feature to to improve your training charge (in english moreover not my natural language).
But when I checked the charts (HR Index and Gradient) on Garmin Connect, I found these data field incredibly useful, more specially the gradient (speed/pace compared to your HR and elevation).
So a big thank you to add this new FITDATA on Connect IQ plateform, Always enabled on my fenix 3.
However I was not enable to display data on long courses (80 km with manual laps), no charts (running dynamics, elevation, HR, HR index or gradient).
5 stars for sure !
2017년 3월 28일 (화), tmatthey71
Thanxs! I did the HR index calculations in the first place for off-line GPS-tracks (https://www.researchgate.net/project/Software-tools-to-enable-geolocated-heart-rate-comparison-from-GPS-activities) and investigated if it was possible to see some trends for common segments, which looks promising. I found out that you need a barometric altitude sensor to be able to compare the HR index between different segments / locations (as you have ;-)). The intention is to add average HR index per lap and include it into the FITDATA, which would give you a very nice history data collection. In case you do not have barometric altitude (e.g., me), you can disable elevation and still get consistent numbers to compare over time; for the same segment.
I'm not sure why you didn't get any data for long courses, if you do not get any data I would rather suspect to be a general issue. Currently I cannot see why HR index should clutter the data for long courses; or the did the HR index crash?
Would be interesting to see some data / charts with barometric sensor and see how elevation plays into HR index calculations. BTW, did you run 80km?!? Impressed.
I published also a running power estimator and pace maker / virtual partner with arbitrary set of lap / segment paces.