Alan Couzens engagement report
@Alan_Couzens - 93K followers on X
Measured over 22 original posts from a 30-day window, last computed on August 26, 2026.
Engagement
A typical post picks up 118 interactions against 93K followers, an engagement rate of 0.127%. Measured over 22 original posts, its engagement rate beats 55% of 3,899 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 18K times each, and 0.653% of those impressions turn into an interaction. That is about 19.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 22 posts sampled, 41% carry an image or video and 27% are part of a thread. The account's strongest tracked post pulled 5.3K interactions, about 45x its own typical post. Recurring topics include #madcrew, #theendurancecode, #bigbooksneedlovetoo.
Measured over 22 original posts from a 30-day window, last computed on August 26, 2026. Recurring tags: #madcrew, #theendurancecode, #bigbooksneedlovetoo.
Compared with accounts its own size
Alan Couzens's engagement rate beats 55% of the tracked X accounts closest to it in follower count (3,899 accounts, accounts of similar size (decile 5 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 34% 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 0.127%, Alan Couzens sits above the 50th percentile of the 37,856 accounts in this comparison. That places it in the above the median band, which runs 0.081% to 0.439%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 155.6% |
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 Tuesday 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% | 53K |
| 01:00 UTC | -2% | 53K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 55K |
| 04:00 UTC | -6% | 44K |
| 05:00 UTC | -4% | 43K |
| 06:00 UTC | -4% | 50K |
| 07:00 UTC | -5% | 54K |
| 08:00 UTC | -4% | 63K |
| 09:00 UTC | -3% | 72K |
| 10:00 UTC | -2% | 75K |
| 11:00 UTC | -3% | 81K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 98K |
| 14:00 UTC | -3% | 101K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 102K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 88K |
| 19:00 UTC | -2% | 83K |
| 20:00 UTC | -1% | 77K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 53K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 238K |
| Monday | 0% | 302K |
| Tuesday | -3% | 302K |
| Wednesday | -1% | 257K |
| Thursday | -2% | 250K |
| Friday | -3% | 259K |
| Saturday | +3% | 233K |
Best tweets
- Aug 18, 202645x their median
Develop the ability to be disliked and free yourself from the prison of other people’s opinions.
- Jul 20, 202611x 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.
- May 1, 20263.7x 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, 20262.9x 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, 20262.7x 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, 20262.5x 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
- Aug 21, 20262.3x 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.
- Aug 22, 20261.9x their median
Just as a rising tide lifts all boats... A rising aerobic base lifts all zones.
- Aug 23, 20261.9x their median
Added a little threshold work back into my week after 12 months of almost pure base. And one thing changed immediately: Hunger. I want to eat ALL. THE. TIME. Way beyond what the extra caloric expenditure would predict. A useful reminder: Exercise doesn't just burn calories. It changes appetite. And harder isn't always better when fat loss is the goal.
- Aug 20, 20261.7x their median
If you have a lot of time available each week, almost anyone can get very fit. If you have very little time available, almost no one can get very fit. Don't blame me. Blame nature.
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.
Buy or sell X accounts - escrow-protected
PlayerSells is an escrow marketplace for X accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 118 interactions against 93K followers, an engagement rate of 0.127%. Measured over 22 original posts, its engagement rate beats 55% of 3,899 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 18K times each, and 0.653% of those impressions turn into an interaction. That is about 19.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 22 posts sampled, 41% carry an image or video and 27% are part of a thread. The account's strongest tracked post pulled 5.3K interactions, about 45x its own typical post. Recurring topics include #madcrew, #theendurancecode, #bigbooksneedlovetoo.
- What is Alan Couzens's engagement rate on X?
- Alan Couzens (@Alan_Couzens) has an engagement rate of 0.127%, based on the median interactions across 22 original posts from the last 30 days against 92,783 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.127%, Alan Couzens sits above the 50th percentile of the 37,856 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 55% of the tracked X accounts closest to it in follower count (3,899 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 Tuesday is its busiest day, at roughly 2.27 posts per day across the measured window.