Why should I log my training?

A training log makes progression checkable. You can see which weights and reps you actually completed, compare trends across several weeks, and base adjustments on data rather than memory. Direct evidence that logging alone causes more muscle growth is limited, though.

What should I record in a training log?

At minimum the exercise, the weight and the reps of every working set. Depending on your training style, RPE/RIR, set type, body weight or brief context notes can be worth adding.

App or notebook — which is better for tracking?

Both can work. Paper is simple and distraction-free; apps can evaluate previous performances, PRs, volume and progression rules automatically. What decides it is whether the system is fast enough that you keep using it.

Which app is good for tracking strength training?

For strength training, what matters most is fast set entry, visible previous performances, reliable offline use and an inspectable history. hitPR is built around exactly that logger workflow and adds automatic progression and analysis on top.

Why You Should Track Your Workouts

You can program a training plan perfectly and still not know whether it works.

If after six weeks all you can say is:

“Bench press is going pretty well, I think.”

then you are missing the most important piece of information.

How much weight? How many reps? On which set? Better than four weeks ago — or just better than last Tuesday?

A training log does not solve that automatically. But it makes it observable.

What tracking shows you that your feel cannot

Your feel is not worthless.

RPE, fatigue, motivation and how the technique felt are training data in their own right.

It gets problematic when feel is the only source of data.

Without recorded performance, you do not reliably know:

whether you manage more reps at the same load
whether your e1RM is climbing across several weeks
how much volume you are actually training
how long an exercise has already been stalling
whether cutting the weight after a bad day was just a reflex or genuinely necessary

Progressive overload does not mean every session has to be heavier than the last.

It means the training stimulus has to keep pace with your rising capacity over time.

You need no perfect database for that.

But you do need some form of memory more reliable than:

“I think it was 80 kg last week.”

What self-monitoring does and does not establish

An important qualification is worth making here.

Self-monitoring research from weight management and general physical activity often gets transferred directly to strength training. That goes too far.

There is good evidence that self-monitoring and digital feedback systems can support physical activity and behaviour change. Wearable and mHealth interventions show small to moderate improvements in activity or training participation in many studies.

A recent systematic review of mHealth-based resistance training also found small positive effects on neuromuscular fitness compared with no intervention, or usual care.

What does not follow from that:

Logging every set therefore demonstrably builds more muscle.

Direct evidence for that is missing.

The stronger reason for a training log is more practical:

Strength training is a repeated decision problem.

You regularly have to decide:

same weight or increase?
same reps or more?
keep the exercise or swap it?
raise volume or reduce it?
deload needed, or just a bad day?

A log gives you the historical basis for comparison.

What to track (and what not to)

More data is not automatically better data.

A good log captures exactly what you will later need for a decision.

Weight, reps, sets — the minimum

For classic strength and hypertrophy programs, these are usually enough:

exercise
weight
reps per working set

The set count falls out of that automatically.

With it you can already:

spot rep PRs
run double progression
follow e1RM trends
compare training volume

You may log warm-up sets too, if they matter for your preparation. You just should not equate them with hypertrophy working sets.

That is the difference between recording something and counting it towards a particular metric.

RPE/RIR — for more advanced lifters

The RPE scale adds an important dimension to weight and reps:

How hard was the set relative to your current capacity?

80 kg × 8 @ RPE 7 is a different state of performance from 80 kg × 8 @ RPE 10.

So RPE/RIR can help with:

autoregulation
judging how the day is going
comparing sets of equal weight
programs with explicit RPE targets

Beginners do not have to leave RPE out. They should just know that the estimate can be less accurate at first.

There is no sensible rule that you may only start after exactly one or two years of training.

Notes — sleep, nutrition, how it felt

Context data is valuable when it answers a question later.

Examples:

“shoulder pulls only on the wide grip”
“4 hours of sleep”
“new belt”
“deadlift with a pause”
“first time without straps”

You do not have to rate sleep, stress and meals on ten scales every day.

A short log of unusual conditions is often more useful than a huge dataset you never look at again.

What you do NOT have to track

For most people who lift, this data is not compulsory:

estimated calorie burn of a strength session
heart rate for every set
exact rest times to the second
subjective scores that never feed into a decision
tonnage for the sake of tonnage

That does not mean those numbers are always useless.

The question is:

Will this metric change a training decision later?

If not, you do not have to record it.

Paper vs. app — an honest comparison

Both can make a good training log.

Paper notebook:

immediately understandable
no distractions
independent of battery or software
very flexible for free-form notes
analysis has to be done by hand

App:

previous performances visible straight away
automatic PR detection possible
volume and trends can be calculated
progression rules can be automated
data stays easier to search
bad UX can be more annoying between sets than paper

So an app does not win automatically by being digital.

It only wins when it creates less work than it takes off your hands.

With training volume per muscle group in particular, automation can be worthwhile: compound lifts, direct and indirect muscle involvement, and week-to-week comparisons get messy on paper very quickly.

3 tracking mistakes (and how to avoid them)

1. Tracking too much

If every set needs weight, reps, RPE, tempo, rest time, mood, pump, pain, grip and five more variables entered, the log itself becomes a training block.

Start with the smallest data basis your progression rule needs.

Usually that is:

exercise + weight + reps.

Everything beyond that gets a clear purpose or stays optional.

2. Never looking back

Storing data is not yet training management.

The value comes from the comparison.

Not necessarily daily — a short look every few weeks is often enough:

Which exercises are climbing?
Where has performance stalled?
Which exercises kept getting swapped?
How has the volume changed?
Were bad phases isolated outliers or a trend?

A good history makes spotting a plateau considerably more sober.

You then react to a pattern rather than to one bad session.

3. Counting warm-ups

Warm-ups are training — but not the same kind of training as your working sets.

If your log contains seven bench press sets, four of them warm-ups, a volume analysis must not automatically turn that into seven hard chest sets.

So set types should stay separate.

That way you can keep recording warm-ups without inflating your hypertrophy volume artificially.

Conclusion

A training log is no magic muscle-building hack.

And the research does not allow the claim that merely recording a set automatically produces better strength or hypertrophy results.

Its value is more concrete:

It makes your training comparable.

You can see what you actually did, tell a trend apart from an off day, and base progression decisions on a history rather than on memory.

So the best log is not the one with the most metrics.

It is the log you use without friction during training, and that later shows you exactly the information you need for the next decision.

Further reading:

Sources

Cox ER et al. (2025). Effects of mHealth interventions to prescribe resistance training: a systematic review and meta-analysis of randomized controlled trials. International Journal of Behavioral Nutrition and Physical Activity.
Ferguson T et al. (2022). Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. The Lancet Digital Health, 4(8):e615–e626.
Krukowski RA et al. (2024). Impact of feedback generation and presentation on self-monitoring behaviors, dietary intake, physical activity, and weight: a systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity.
Zourdos MC et al. (2016). Novel Resistance Training-Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve. Journal of Strength and Conditioning Research, 30(1):267–275.

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