Alan Couzens engagement report
@Alan_Couzens - 93K followers on X
Measured over 38 original posts from a 30-day window, last computed on September 19, 2026.
Engagement
A typical post picks up 123 interactions against 93K followers, an engagement rate of 0.133%. Measured over 38 original posts, its engagement rate beats 56% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 1.00% of those impressions turn into an interaction. That is about 13.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.5 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 13:00 UTC, and Sunday is the busiest day of the week. Of the 38 posts sampled, 34% carry an image or video, 24% are part of a thread and 8% link out. The account's strongest tracked post pulled 5.3K interactions, about 43x its own typical post. Recurring topics include #theendurancecode, #madcrew, #thestrengthcode.
Measured over 38 original posts from a 30-day window, last computed on September 19, 2026. Recurring tags: #theendurancecode, #madcrew, #thestrengthcode.
Compared with accounts its own size
Alan Couzens's engagement rate beats 56% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 7 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 41% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.133%, Alan Couzens sits above the 25th percentile of the 212,925 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.698% |
| 90th percentile | 2.61% |
| 99th percentile | 83.0% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 13:00 UTC, and Sunday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 34% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 8% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 34% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 8% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 255 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Aug 18, 202643x their median
Develop the ability to be disliked and free yourself from the prison of other people’s opinions.
- Jul 20, 202610x their median
Most exercise and aging studies can't answer a basic question: is muscle deterioration from aging itself, or just from decades of moving less? A new Nature Aging study solved this by recruiting older adults who moved as much as people in their twenties. The researchers from Amsterdam UMC and Maastricht University recruited four distinct groups: young adults in their twenties, older adults whose daily step counts and high-intensity activity matched the young group, older adults who had trained consistently for years (three structured hour-long sessions per week for over a year), and older adults with early physical impairment. They took muscle biopsies before and after a one-hour cycling session, then measured over 24,000 gene transcripts, 135 metabolites, and 1,383 lipid species. By matching activity levels between young and older groups, any molecular differences couldn't be blamed on the older adults simply moving less. This isolated aging from inactivity for the first time at this molecular depth. Key findings: • The defining molecular signature of muscle aging is an energy crisis. Comparing young adults to activity-matched older adults, 1,106 genes were downregulated with age. These genes build the mitochondrial machinery that produces cellular energy: ATP synthase, cytochrome c oxidase, and NADH dehydrogenase subunits. Mitochondria are the power plants of cells, converting nutrients into ATP, the energy currency cells use to function. When these genes decline, cells lose their ability to generate energy efficiently. • NAD+ levels declined and triglycerides accumulated inside aging muscle. NAD+ is a molecule required for energy production and cellular repair. Lower NAD+ means less capacity to convert fuel into usable energy. Triglycerides are stored fats, their accumulation inside muscle indicates unburned fuel piling up as the tissue loses its ability to process it. • More than half the molecular signature of muscle aging was absent in trained older adults. Specifically, 57.1% of age-related gene downregulation and 55.9% of upregulation were missing in the trained group. Their muscle resembled young adults far more than their chronological age would predict. • The changes training preserved were specifically the energy metabolism ones. Genes like NDUFS1 and COX5A, which were depleted in normally active and impaired older adults, sat at youthful levels in the trained group across all five mitochondrial respiratory complexes. The single most prominent feature of muscle aging turned out to be the single most preventable. • Being generally active was not enough. Structured training was the difference. The normally active older adults walked as much as young adults, and their energy metabolism genes declined anyway. What preserved the youthful molecular profile was structured, sustained training. Filling a step counter and being genuinely trained are not equivalent at the molecular level. • Roughly half of muscle aging persisted regardless of training. Changes in genes controlling synaptic transmission (how nerves communicate with muscle) and WNT signaling (a pathway regulating tissue maintenance and stem cell function) appeared in all older adults, trained or not. This unavoidable half is where drugs will have to work. • The fittest muscle mounted the largest inflammatory response to exercise. All groups activated stress and immune genes after exercise, including IL6, IL1B, and TNF. But the magnitude scaled with fitness. Trained older adults most closely resembled young adults in their response, followed by normally active, with impaired older adults showing the most blunted response. The stress response to exercise appears to be the mechanism of adaptation, not damage to be minimized. This raises a concern about anti-inflammatory longevity strategies. If the inflammatory stress response is how exercise produces its benefits, chronically suppressing inflammation may blunt the adaptation that exercise depends on. It doesn't mean inflammation is beneficial in general, but the timing and context matter. A separate discovery: the proteasome appears to regulate NAD+. The proteasome is the cellular machinery that breaks down damaged proteins. When researchers inhibited it, NAD+ levels rose in both muscle and liver cells to a degree comparable to NAD+ precursor supplements. This opens a new route to understanding NAD+ decline that operates through protein turnover rather than just supplying more raw material. The study draws a clear line between what lifestyle can address and what will require therapeutics. The energy metabolism decline, mitochondrial deterioration, and NAD+ depletion that define muscle aging are largely preventable through structured training. The synaptic and signaling changes that persist in all older adults represent the unavoidable half where drugs will need to work. The decisions made about structured training in midlife determine which molecular trajectory muscle follows in later decades. Half of muscle aging is optional. The other half isn't. Knowing which changes belong to each category is knowing where behavior ends and biology takes over.
- Sep 8, 20265.7x their median
I knew the patience that endurance training taught me would prove useful... Four years of hard work, but she finally arrived! Now taking up residence among some esteemed company - right where she belongs. I’ll be going through the proof today and, if all looks good, placing the first order of signed copies to go out to the founding members on my ’stack. A massive thank you to you all for the support along the way. 🙏
- May 1, 20263.6x their median
A warm welcome to the new followers 👋 Thanks for hitting the "follow" button 🙏 If you’re here, you probably care about improving your CV fitness &/or getting better at endurance sport - without the noise. I’m Alan Couzens: Coach. Exercise physiologist. Builder of things that try to make coaching scale. For the last ~30 years I’ve worked with everyone from beginners → World Champions, mostly focused on one question: What actually makes athletes fitter and faster in the long-term? A few things you’ll hear me talk about a lot: → "Real" fitness is slow and laggy. If you can see it, it's not lasting. → Most athletes sabotage themselves by mixing volume + intensity too early → Easy training is a skill (and most people do it wrong) → More training isn't the goal. More fitness is. I write deeper dives here in my online book (models, case studies, practical takeaways): 👉 https://t.co/CYgd4hQXTA And we get into the weeds here with athletes & coaches: 👉 https://t.co/DqbWZ56ui8 If you're ready to think about training a little differently - Welcome!
- Jul 27, 20262.7x their median
Putting together the figures for my next post on the ’stack… Your 50-Year Training Plan Your most important training-load ramp isn’t the one that prepares you for your next race. It’s the one that keeps you training for the rest of your life! Not for your next race. For your last decade.
- Aug 17, 20262.6x their median
My upcoming book #TheEnduranceCode is so big that I had to move the appendices, references, and index online! Putting the website together right now. Pretty! 😍 https://t.co/IJk1Cnbx9C
- Aug 18, 20262.4x their median
How I look at training in a nutshell ... As a cost/benefit, risk/reward proposition. On special occasions, you might be in a good position to tolerate risk. Most days you aren't. https://t.co/v6OswyZ5Zl
- Sep 10, 20262.3x their median
In light of yesterday's discussions around A.I. risk... Remember, the first rule of surviving the apocalypse... Cardio.
- Aug 21, 20262.2x their median
"How do I know if I'm burning a large amount of fat while training?" 🔥 a) Get a metabolic test. A distant b)... Breathing through mouth? Carbs. Breathing through nose only? Fat. It's not a perfect rule, but it's a good start.
- Sep 13, 20262.1x their median
Look at how narrow Jakob's shoulder are in his latest insta... Can you imagine if instead of deciding to apply himself to running, he chose swimming? Even if he did the same training over all of those years, the results would have been very different. Yet again showing the importance of selecting the right sport for your body type... You don't choose your sport, your sport chooses you!
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 123 interactions against 93K followers, an engagement rate of 0.133%. Measured over 38 original posts, its engagement rate beats 56% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 1.00% of those impressions turn into an interaction. That is about 13.2% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.5 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 13:00 UTC, and Sunday is the busiest day of the week. Of the 38 posts sampled, 34% carry an image or video, 24% are part of a thread and 8% link out. The account's strongest tracked post pulled 5.3K interactions, about 43x its own typical post. Recurring topics include #theendurancecode, #madcrew, #thestrengthcode.
- What is Alan Couzens's engagement rate on X?
- Alan Couzens (@Alan_Couzens) has an engagement rate of 0.133%, based on the median interactions across 38 original posts from the last 30 days against 92,903 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.133%, Alan Couzens sits above the 25th percentile of the 212,925 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @Alan_Couzens have real engagement?
- Its engagement rate beats 56% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @Alan_Couzens post?
- Most posts go out around 13:00 UTC, and Sunday is its busiest day, at roughly 3.5 posts per day across the measured window.