GrowthTrack Analysis: Does Using Heat Help? Yes - But Don't Overcook Your D!
Does using heat substantially help your PE progress? The straight answer is that it does, but that there's definitely such a thing as "too much of a good thing" - overusing heat is counterproductive, and there is a sweet spot or "Goldilocks Zone".
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If you want a very brief primer on why people use heat for PE, it is because of two main things; first, it slightly affects the viscoelastic properties of the collagen matrix - partly through the way our cells react to heat, partly because of the physical effect of heat on the strength of chemical bonds and how much energy is required to break them. Succinctly, it makes it a little easier to stretch or expand the penis with less force applied. Not a huge difference as some have claimed, but enough to matter. Second, heat affects the biological adaptation to stress; it triggers heat shock proteins, it recruits parts of the immune system, some amount of heat is anti-inflammatory.
If you want more than this very cursory glance at the subject, I have written in much greater depth about the effects of heat in my post about Kyrpa's ultrasound protocol here:
https://www.reddit.com/r/TheScienceOfPE/comments/1kekvmz/kyrpas_protocol_therapeutic_ultrasound_in_penis/ (this applies to non-ultrasound heat as well)
u/PatientGains writes about another aspect of Kyrpa's protocol here:
https://www.reddit.com/r/TheScienceOfPE/comments/1jki5bf/length_workout_optimization_principles/
I have written about the dangers of over-exposure to heat here:
https://www.reddit.com/r/TheScienceOfPE/comments/1ks4yk8/psa_heat_pad_damage_are_you_slowly_cooking_your/
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It's been widely regarded as an established truth in the PE community that using heat can help with faster gains, or getting you unstuck if you have plateaued. But previous GrowthTrack results have surprised and debunked myths. When I built the app, one of the very first features I built tracking for was use of heat and vibration (independently, or in combination). Now I believe we have enough longitudinal data to get a preliminary result.
When building my analysis page for heat use, I used what has now become my go-to battery of statistical tests. My aim was to see whether there is any linear relationship between use of heat and my three standard gain outcomes: absolute gains, gain rate per hour of PE, gains per month, and also whether there is a quadratic relationship such that there are diminishing returns. To test the latter, I included a scatter plot with linear and quadratic regression tests, and importantly; an ANCOVA test to check whether any detected quadratic relationship (in gain rate per month) is independent of workload.
Users needed to have logged 100+ sessions in the relevant categories to be included. Gain rates faster than 2mm/hour were excluded as measurement/unit errors as usual. For the MSEG analysis, the categories "girthwork", "hybrid", and "EQ work" were included in the session and workload count. For the BPEL analysis, all of the above plus "lengthwork" and "ADS" were included in the count. There is significant overlap of users for the two analyses, but not all have data for both MSEG and BPEL. Gains prior to logging sessions with the app were not included.
Heat use was defined as: "Heat % = time on routine_exercises with use_heat = true divided by total tracked exercise time." So if someone uses 75% heat, it's not that they use heat in 75% of their sessions, it's that 75% of the time they spend doing PE is with heat applied. They're really keeping their dicks warm most of the time!
I ran the analysis twice for both outcome types, to test for robustness to perturbations in bucket cut-offs;
-Once with "No Heat" defined as <5% use of heat and "Low Heat" defined as 5-40% use of heat, and hence "High Heat" >40%.
-Once with the buckets <10%, 10-50%, and >50%.
The results were robust to such perturbations - I am only going to show you the outcome of the second round with the 10% and 50% cut-offs, and leave you with a "trust me bro"; the outcomes were the same across the board in terms of which tests showed statistical significance, with only negligible differences in effect size.
As usual, I ran a group confounder check to test whether groups were similar in terms of ages, prior PE experience (an important check to exclude newbie/veteran discrepancies affecting the outcome), physical fitness and lifestyle factors.
With these methodological concerns out of the way, let's take a look at the results:
MSEG
Here we see that the "low heat" group, where users had applied heat for on average 28.5% of their total PE hours, appear to have had the best total growth and the best growth per month, with the 95% CL bars barely overlapping with the "No Heat" and "High Heat" groups. All groups had logged on average between 55 and 60 hours, but the "Low Heat" group had done so within a shorter average time span.
The latter is interesting; why this difference in time span? I have no good answer to that!
Note that the "No Heat" group is aptly named, even though I allowed for up to 10% heat use; their average is 1.2%, meaning the vast majority of the group have done zero heated work.
The "High Heat" group is also aptly named - their average sits at 77.8% heat use. Again, this is 77.8% of their total "time under tension" with heat applied to their penis.
We see that the "Low" and "High" groups are roughly equal in terms of mm/hour, but that the variance is greater in the High group (larger CL-bar).
But are these differences in absolute gains and gains per month statistically significant? I'm glad you asked:
P-values are significant for both ANOVA and Kruskal-Wallis with decently large effect sizes for both total growth and growth per month. The pairwise checks for total growth survived Bonferroni correction for multiple comparisons for both Welch t-test and Mann-Whitney. (If you want more details about these tests, see my previous analyses.)
Now to test whether this difference in growth per month was independent of workload. I performed an ANCOVA (ANalysis of CO-VAriance)
The P-values here show that Low Heat still outperforms No Heat even when workload is accounted for. We see that weekly workload is contributing significantly to gain rate.
And here is the main outcome I intended to test; whether "Low Heat" outperforms both the no-heat and high heat groups. It does, and it survives correction for workload. Weekly workload is contributing to gain rate as before.
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Perhaps this is a good place to pause and take stock?
When it comes to girthwork, applying heat during sessions appears to be a good thing. But as the results here show, we should not be applying heat every session. The reason I don't say "the whole session" is that girthwork sessions are generally short - the average in the app being somewhere around 15 minutes, which is how long it takes to get the penis hot.
I would like to inject a little caveat emptor here; heat does give temporary swelling - it increases blood vessel permeability, meaning more fluid leaks out into the interstitial space, i.e. we get more edema. But users should wait for days after a session before updating baseline measurements, so this should not be a factor. And if it was a factor, it should be expected to affect the "high heat" group even more than the "low heat" group... So probably this is an error source we can disregard. I just thought I should mention that I have considered it.
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Do we find the same thing in BPEL data?
I will just give you the screenshots and let you interpret them, now that you know what to look for:
Middle group: Less overall workload, shorter time span, more growth per hour and per month and more total growth. But error bars overlap to a large degree. Growth per month seems to have the least overlap though - especially for the "high heat" group... Let's look at the omnibus tests:
Yup. As expected - only growth per month is statistically significant for BPEL.
No heat vs low heat comes close, but does not survive Bonferroni correction. High heat performs significantly worse than low heat, however. So how does this look when we compensate for workload?
Well, here again we see that "low heat" outperforms "no heat" and "high heat" even when taking workload into account. As before, weekly workload correlates positively with growth, and the result is statistically significant, even though the effect is not quite as strong for length as for girth.
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Conclusion?
Well, it's getting rather late here - it's 1 AM as I am writing this, and I'm an old man and getting tired. I want to post before I go to bed, so that I can wake up to a stream of comments and objections :D
I will keep it short:
Once again, we see the power of large datasets for PE studies. This time, I didn't debunk a long held belief as I did with PDE5i and Citrulline supplements (which turned out to be pretty pointless for PE gains in the grand scheme of things) - this time the belief that heat can improve PE gains turned out to be in line with actual results.
But importantly, we see that this doesn't mean you should go ahead and apply heat every session, or for the whole session.
I have not found an exact optimum, but looking at scatter plots for both BPEL and MSEG and heat application, the Goldilocks amount of heat application appears to be around 20-30% of the total PE time.
I mentioned Kyrpa in the beginning... his protocol evolved over time, but in PatientGain's summary it boiled down to:
Conditioning stretch: 30-40 minutes
Heated stretch: 20-25 minutes
Cooldown stretch: 10 minutes.
That is between 28-38% heat application in a single lengthwork session.
Add an EQ-work session on rest days or before bed, and you'll be down to the Goldilocks range.
If you mainly do girthwork and you do 7 hours of pumping per week, let 1.5 hours be heated, and you will be in the Goldilocks range. That means 4 sessions where you spend 20 minutes heated.
That actually coincides with the amount of NIR heating that has been well studied when they have looked at wound healing and inflammation, 3-4 sessions of 20 minutes heat per week.
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Succinctly: Use heat, but not all session every session - don't overcook your D. Heat it until tender, not charred :)
/Karl - Over and Out
Ps.
A Small Plea For Support:
GrowthTrack is now large enough that hosting, database traffic, and development costs are becoming a little too noticeable (to my wife). I have created a Patreon for people who want to help keep the app free, ad-free, and independent. No features are ever moving behind a paywall; the goal is simply to offset some of the recurring costs while continuing to build better analyses and eventually structured in-app trials. If you want to support that work, the link is here:
https://www.patreon.com/posts/support-158235429
The best way to support the project is still to contribute your data:
A huge thank you to all who have contributed thus far, and especially to the many users who have logged their PE sessions and measurements in the app. Without their diligent logging efforts, none of these analyses would be possible.
Together, we are debunking myths and confirming best practice, nailing down appropriate pressure and tension ranges and session cadence and weekly workload, etc, etc. We are taking PE out of the age of Bro-Science and into the age of ... the science of PE.
