What does the Detraining Decay Estimator calculate?

Methodology for the Detraining Decay Estimator: Mujika & Padilla model, training-age scaling, and retraining recovery.

What inputs does the Detraining Decay Estimator require?

It takes the following inputs: prior 1rm, weeks off, training age years.

What does the Detraining Decay Estimator return?

It returns: predicted1RmAfterWeeksOff, pctLossTotal, decayRatePerWeek, asymptotePctOfPeak, retrainingWeeksToPeak, weeklyTrajectory.

Is the Detraining Decay Estimator free to use?

Yes. It runs entirely client-side in your browser with no signup, and is also importable as an ES module engine for AI agents.

What category does the Detraining Decay Estimator belong to?

Planning. See the methodology above for formulas, assumptions, and limitations.

Methodology: Detraining Decay Estimator

Scope

Estimates 1RM decay across a training layoff. Returns predicted strength after the gap, weekly trajectory, and rough recovery time once retraining starts.

Formula

Exponential decay toward an asymptote (residual strength floor):

x(t) = asymptote + (peak − asymptote) × exp(−k × t)

where k = −ln(1 − decay_rate) tunes the curve so the first-week loss equals the band's headline rate.

Coefficients (training-age bands)

Training age	Decay/wk	Asymptote
< 1 yr	5%	50%
1–3 yr	3.5%	60%
3–7 yr	2.5%	70%
≥ 7 yr	1.8%	75%

Data sources

Mujika I, Padilla S. Detraining: loss of training-induced physiological and performance adaptations. Part II: Long term insufficient training stimulus. Sports Med. 2000;30(3):145-154. — PMID 10999421. Headline strength-decay rates by training-age band.
Mujika I, Padilla S. Cardiorespiratory and metabolic characteristics of detraining in humans. Med Sci Sports Exerc. 2001;33(3):413-421. — Companion study on aerobic decay; informs the asymptote shape.
Mujika I. The alphabet of sport science research starts with Q. Int J Sports Physiol Perform. 2013;8(5):465-466. — Practical retraining heuristics.

Assumptions

Layoff means no resistance training. Light activity (walking, easy cycling) doesn't change decay rate materially.
Retraining recovery uses a 1-week-per-2-weeks-off heuristic, capped at original training-age length.
Bands are coarse — a 4-yr lifter is identical to a 3-yr lifter under the model.

Approximation range

Real decay varies ±30% per individual. Females, older lifters, and dieting populations decay faster; well-fed, lean-mass-rich lifters decay slower.

Limitations

Does not model muscle memory pathways (myonuclei retention, Gundersen 2016) which speed retraining.
Treats 1RM as the only output; hypertrophy decays faster than strength in young lifters.
Asymptote is an empirical floor, not a guarantee — beyond 6 months off, decay continues slowly past the predicted asymptote.

Reproducibility

1RM 150 kg, 4 weeks off, 3 yr training age: band = 2.5% decay, 70% asymptote. asymptote = 105 kg. k = −ln(0.975) ≈ 0.0253. predicted = 105 + (150 − 105)×exp(−0.0253×4) ≈ 145.6 kg. Loss ≈ 3%.

Change log

2026-05-08: methodology page first published.

One-Rep Max Calculator — Estimate the prior 1RM you feed in.
Wendler 5/3/1 Planner — A safe template for the retraining block.

Worked example

Computed by the same engine bundle served at /engines/detraining-decay.js. Re-runnable: the values below are the literal output of compute(engineInput).

Input

tool: detraining_decay
prior_1rm: 150
weeks_off: 4
training_age_years: 3

Output

prior1Rm: 150
trainingAgeYears: 3
decayRatePerWeek: 0.025
asymptotePctOfPeak: 70
predicted1RmAfterWeeksOff: 145.7
pctLossTotal: 2.9
weeklyTrajectory: [{"week":0,"predicted1Rm":150,"pctOfPeak":100,"pctLossWeekly":0},{"week":1,"predicted1Rm":148.9,"pctOfPeak":99.3,"pctLossWeekly":0.8},{"week":2,"predicted1Rm":147.8,"pctOfPeak":98.5,"pctLossWeekly":0.7},{"week":3,"predicted1Rm":146.7,"pctOfPeak":97.8,"pctLossWeekly":0.7},{"week":4,"predicted1Rm":145.7,"pctOfPeak":97.1,"pctLossWeekly":0.7}]
retrainingWeeksToPeak: 2

FAQ

What does the Detraining Decay Estimator calculate?: Methodology for the Detraining Decay Estimator: Mujika & Padilla model, training-age scaling, and retraining recovery.
What inputs does the Detraining Decay Estimator require?: It takes the following inputs: prior 1rm, weeks off, training age years.
What does the Detraining Decay Estimator return?: It returns: predicted1RmAfterWeeksOff, pctLossTotal, decayRatePerWeek, asymptotePctOfPeak, retrainingWeeksToPeak, weeklyTrajectory.
Is the Detraining Decay Estimator free to use?: Yes. It runs entirely client-side in your browser with no signup, and is also importable as an ES module engine for AI agents.
What category does the Detraining Decay Estimator belong to?: Planning. See the methodology above for formulas, assumptions, and limitations.