Training · body composition

Client name: Bill

Did I have better body comp progress (more lean muscle, less fat) when I got more training stimulus?

Presenting Question

Question Verbatim from You: “Did I have better body comp progress (more lean muscle, less fat) when I got more training stimulus?”
Interpretation of the Question (an attempt to make the question slightly more precise, computable and scientific)
- In your question you said “body comp progress (more lean muscle, less fat)”. We are measuring that as lean mass going up and fat mass going down, whole body, from your DEXA scans at BodySpec.
- “More training stimulus” we are interpreting as more sets a week, counting every set in the Strong app and, for the year it was used, the Trainerize app. You take every set to failure and log no warm-ups, so each set counts as a hard one.
- Precisely: across six-week blocks between your DEXA scans, did blocks with more sets a week bring more lean gain, or more fat loss, than blocks with fewer?

Answer to the Presenting Question: A trend

A trend, based on the analysis below, and within its assumptions and limits.

Lean mass: blocks with more sets brought more lean gain, about +0.23 lb per two weeks for every 10 extra sets a week.
That passes the test taken alone, but not once it is counted as one of the six questions tested together on the same history.
Fat mass: no change linked to sets.
So a lead worth watching on scans still to come, not a finding.

Do you have a similar question? Join the waitlist.

In blocks when you lifted more, did you gain more lean mass?

Each of your 12 six-week blocks, split within its epoch into the ones with more sets a week than that epoch's middle block, and the rest.

Lean mass, the 5 blocks with more sets: +0.11 lb per two weeks on average.
Lean mass, the other 7 blocks: −0.65 lb per two weeks.
Fat mass, the 5 blocks with more sets: +0.18 lb per two weeks.
Fat mass, the other 7 blocks: +0.09 lb per two weeks.

Lean mass, one dot per block · lb per two weeks

Fat mass, one dot per block · lb per two weeks

The lean dots climb a little from left to right. The fat dots barely move.

For p-values, corrections for testing many questions, effect sizes and the smallest detectable differences, see The statistics behind this below.

Data Source(s) and Date(s)

Data sources:
- Devices:
  - DEXA scanner (BodySpec) data: whole-body lean and fat mass, with one scanner's known offset removed first
  - Strong app data workout log, exported 2026-10-01: sets from 2024-03-15
  - Trainerize app workout log, exported 2025-11-01: the sets for 2024-11-02 to 2025-10-17, when the Strong app was not in use
- Textual sources:
  - Your conversations with PerSci, 2026-10-01 to 2026-10-27: when each trainer started, that every set you log is taken to failure, and what the Trainerize session ratings were.
Dates: 2024-04-05 to 2026-09-15: 12 six-week blocks. The three Epoch B blocks before 2024-03-15 are left out: you trained then, but nothing was logged.
People and Organizations Named or Anonymized:
- Named (in your template names, your notes, or what you told us): BodySpec
- Anonymized (shown by alias, inside your own notes and quotes too): Trainer A (personal trainer, 2023-08-08 to 2024-11-10), Trainer B (personal trainer since 2024-11-11)

What we compared (independent variable)

How many sets you did a week, on average, across each block.

Sets a week per block: 39 to 113
Compared only within an epoch, never Epoch B against Epoch C

What we measured (dependent variable)

How your body composition changed over each block.

Lean mass, change per two weeks
Fat mass, change per two weeks

The test written down beforehand: A trend

Registered on 2026-10-25; how sets are counted was fixed on 2026-10-27, before this ran. Lean mass: about a 1-in-23 chance of a link this strong by luck alone (p = 0.044, raw). Allowing for the six questions tested together on the same history, the corrected value is 0.26, well short of the bar.

Run three other ways, chosen in advance, the lean link keeps its direction, from +0.15 lb to +0.27 lb per two weeks per 10 sets, and is weaker: none of the three passes on its own.

For p-values, corrections for testing many questions, effect sizes and the smallest detectable differences, see The statistics behind this below.

Caveats

This is an association. Weeks with more sets may also have had more protein or food. The food log has a nine-month gap (2025-01 to 2025-09), so that cannot be checked yet. Sleep and steps can be, and holding them steady finds the same link (Related Pages).
Most of the link comes from Epoch C, which has 9 of the 12 blocks.
This describes a pattern in your data. It is not medical advice.

Did I have better body comp progress when I had been sleeping more? — sleep against the same six-week body composition changes
Did I sleep better when I worked out harder? — the other direction: the day's sets against that night's sleep
Of my sleep, training and steps, what goes with lean gain and fat loss? — sleep, sets and steps in one model, on the same blocks
What predicts my sleep? — everything logged each day against that night's sleep
What does my DEXA data look like? — every scan, the measurements these changes are computed from
What does my Oura data look like? — every night's sleep, described without any question asked of it
What does my Strong App data look like? — every logged session and set

The statistics behind this

As on the sleep page, with sets per 7 days as the lever: slope per 10 sets a week, permutation p within epoch (20,000), rough bootstrap range, smallest detectable slope, Holm across the six questions tested together on the same history. No day appears in both the Strong and Trainerize logs; building the lever refuses if one does.

Run	Outcome	Blocks	Per 10 sets/wk, lb / 14 d	Rough range	Detectable	p	Holm (6)
registered	lean mass	12	+0.23	+0.07 to +0.41	0.22	0.044	0.26
registered	fat mass	12	-0.06	-0.15 to +0.02	0.16	0.546	1.00
illness periods dropped	lean mass	10	+0.27	-0.05 to +0.47	0.32	0.121	—
illness periods dropped	fat mass	10	-0.15	-0.34 to -0.02	0.25	0.293	—
every scanner offset removed	lean mass	12	+0.15	+0.01 to +0.33	0.20	0.148	—
every scanner offset removed	fat mass	12	-0.09	-0.16 to +0.02	0.17	0.387	—
blocks sharing a scan	lean mass	15	+0.21	-0.04 to +0.42	0.26	0.118	—
blocks sharing a scan	fat mass	15	-0.06	-0.18 to +0.09	0.20	0.614	—

Detectable: A real change this big would just pass the test, about half the time. One about 1.4 times as big would pass it about 4 times in 5. A smaller real change could easily go unseen, so 'no change found' means 'no change this big'.

What do you want to know next?

Each one runs on your own export. What you asked is written down with today's date before anything is computed, so the question stays separate from the answer.

Check it on scans still to come

Everything on this page uses history you had already lived. The same test, the same code, on six-week blocks that start after the registration (2026-10-25) is the real check. At a scan every two to six weeks that takes a year or more.

Nothing but to keep scanning and logging as you do.

Running from 2026-10-25

Add food: protein, calories and alcohol

The full question puts sleep, sets, protein, calories, steps and alcohol in one model. Only 5 of the 15 blocks have food logged (none from 2025-01 to 2025-09), too few for six levers, so it runs on blocks still to come.

Log food every day in MacroFactor.

Waiting for data

If you have a health-related question about your own data, join the waitlist for your own custom analysis.

Act soon: it is free while we stand this service up and refine it. All we ask in return is permission to publish a results page like the ones we have made for our other clients. We never publish your surname or your date of birth. The page shows a first name or a pseudonym, whichever you prefer, and your age in years and your sex.

Made by PerSci from your DEXA scans and your Strong and Trainerize logs. It describes a pattern in your data; it is not medical advice.

PerSci is not medical advice. Analyses describe one person's own data and are not a diagnosis or a treatment recommendation. Full disclaimer · Privacy Policy