Hypertrophy Evidence Notes
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A System for Keeping a Log

Logging alone is not enough; feedback against your goals is what works. Keep it under 30 seconds, tie it to a cue, and don't punish missed days.

3731Last reviewed Verdicts

Verdicts on this page14
  1. AStrong evidenceMonitoring progress makes goals more likely to be reached. The effect is larger when you physically record it on paper or in a file, and larger still when you make it public.
  2. AStrong evidenceSpecific “if X happens, I will do Y” plans (implementation intentions) improve goal attainment by a medium to large amount. For exercise alone, the effect is smaller.
  3. BProbably trueThe median time to form a habit is about 2 months. Individual variation is very large, from 4 to 335 days.
  4. CMay be trueMissing one day has little effect on habit formation.
  5. BProbably truePeople keep going when they enjoy it or are intrinsically motivated. Feeling forced does not last.
  6. BProbably trueTracker use drops over time: 74% after 100 days, 16% after 320 days. The main reason for quitting is technical problems.
  7. CMay be truePeople who weigh themselves daily lose more weight, with no sign of psychological harm.
  8. DUncertain, inferenceDaily body weight moves with water, glycogen, salt, and gut contents. Use only the 7-day average and its week-to-week change for decisions.
  9. AStrong evidenceSetting goals changes behavior. The effect is larger when goals are difficult and when they are made public.
  10. BProbably trueSelf-reported RIR is off by about 1 rep on average. It is more accurate closer to failure and less accurate on high-rep sets.
  11. BProbably trueGamification increases physical activity, but the added effect is small and shrinks further once the intervention ends.
  12. BProbably trueShowing lifting results visually, right away, improves performance. Visual feedback works better than verbal.
  13. CMay be trueHighlighting a broken streak lowers later engagement. Some people keep training injured to protect a streak.
  14. BProbably trueRewards for showing up lower intrinsic motivation. Positive feedback about competence raises it.

Bottom line

You already have a training habit. What you need to build is a logging habit, and you attach it to your training.

  • Keep each log entry to 30 seconds or less. Write only exercise, weight, reps, and RIR
  • Tie it to a cue, such as “after the last set, log before leaving the gym”
  • Weekly feedback (an AI summary and a comparison with your goals) is the part that works
  • Weigh yourself daily, but look only at the 7-day average
  • Missing one day does not break a habit. Just don’t miss twice in a row
  • It takes 2–3 months to settle in. Let the system carry you until then

Logging and feedback

AStrong evidence

Monitoring progress makes goals more likely to be reached. The effect is larger when you physically record it on paper or in a file, and larger still when you make it public.

  • In a meta-analysis of 138 studies and about 20,000 people, progress monitoring improved goal attainment with an effect size of 0.40 (Harkin 2016)
  • In a meta-regression of 122 intervention studies, combining self-monitoring with goal setting or feedback raised the effect size from 0.26 to 0.42 (Michie 2009)

A log committed to git is a physical record, and the weekly review is feedback plus goal review. Publishing the repository or site also makes it public.

Tie it to a cue

AStrong evidence

Specific “if X happens, I will do Y” plans (implementation intentions) improve goal attainment by a medium to large amount. For exercise alone, the effect is smaller.

The cues set in this repository:

  • After the last set, before leaving the gym, log the day’s sets on your phone
  • After waking up and using the toilet, step on the scale and log it

How long a habit takes

BProbably true

The median time to form a habit is about 2 months. Individual variation is very large, from 4 to 335 days.

  • In a systematic review, the median time to habit formation was 59–66 days, the mean 106–154 days, and the individual range 4–335 days (Singh 2024)
  • In a study of 111 new gym members, training at least 4 times a week for 6 weeks was the threshold for habit formation (Kaushal & Rhodes 2015)
  • Simpler behaviors become habits faster (Gardner 2012). This is why logging is kept minimal

CMay be true

Missing one day has little effect on habit formation.

This was observed in Lally 2010’s longitudinal study (see also the summary in Gardner 2012). So there is no streak counter that punishes misses.

Motivation that lasts

BProbably true

People keep going when they enjoy it or are intrinsically motivated. Feeling forced does not last.

  • Adherence to resistance training is linked to enjoyment, self-efficacy, and self-regulation (Rhodes 2017)
  • Better mood during exercise predicts more activity later (Rhodes & Kates 2015)
  • Intrinsic motivation predicted long-term adherence (Teixeira 2012)
  • Autonomous motivation was associated with better health behavior; controlled motivation was not (Ntoumanis 2021)

So the weekly review is written as information: “here is what the data shows.” It always points out PRs and what improved.

People get tired of apps and trackers

BProbably true

Tracker use drops over time: 74% after 100 days, 16% after 320 days. The main reason for quitting is technical problems.

  • Follow-up of 711 activity tracker users (Hermsen 2017)
  • Dropout from app-based interventions was 43% in intervention studies and 49% in observational studies (Meyerowitz-Katz 2020)

With git and plain text, there are no dead batteries, canceled subscriptions, or discontinued apps.

Weigh daily, look at the average

CMay be true

People who weigh themselves daily lose more weight, with no sign of psychological harm.

  • In an RCT using smart scales and email feedback, the daily-weighing group lost 6.55% versus 0.35% in the control group (Steinberg 2013)
  • Daily weighers lost more than those who weighed less often (Steinberg 2015)
  • A review found regular weighing was not associated with depression, anxiety, or other harms (Zheng 2015)

All of these studied people losing weight. Applying them to bulking or cutting with resistance training is an inference.

DUncertain, inference

Daily body weight moves with water, glycogen, salt, and gut contents. Use only the 7-day average and its week-to-week change for decisions.

Goal setting

AStrong evidence

Setting goals changes behavior. The effect is larger when goals are difficult and when they are made public.

  • The unique effect of goal setting was an effect size of 0.34 (Epton 2017)
  • Goal-setting interventions for physical activity had an effect size of 0.55 (McEwan 2016)

Use two layers of goals.

  • Process goals: log every session, weigh in at least 6 times a week
  • Outcome goals: +5kg estimated 1RM on the main lifts in 12 weeks, body weight +0.25% per week

What to log

BProbably true

Self-reported RIR is off by about 1 rep on average. It is more accurate closer to failure and less accurate on high-rep sets.

Meta-analysis (Halperin 2022). In experienced lifters, RPE correlates strongly with lifting velocity (Zourdos 2016). If writing RIR for every set is a chore, the last set is enough.

How progress is shown

The workout screen and the records page on this site are built on the findings in this section.

BProbably true

Gamification increases physical activity, but the added effect is small and shrinks further once the intervention ends.

  • A meta-analysis of 16 RCTs found an effect size of 0.42. Against active control groups it was 0.23, and about 14 weeks after the intervention ended it was 0.15 (Mazeas 2022)
  • A megastudy of 54 interventions in 61,293 gym members found that only 8% of the interventions that raised visits kept working after the 4-week program. The best one was a small reward for coming back after a missed workout (Milkman 2021)

BProbably true

Showing lifting results visually, right away, improves performance. Visual feedback works better than verbal.

A meta-analysis of 20 studies found that feedback raised bar velocity by about 8.4%. Visual feedback had an effect size of 1.11, verbal 0.47 (Weakley 2023). That is why the workout screen shows the difference from last time and any personal record the moment a set is done.

CMay be true

Highlighting a broken streak lowers later engagement. Some people keep training injured to protect a streak.

  • Across 7 studies, showing an intact streak increased later engagement and showing a broken one decreased it. The effect was stronger when people blamed themselves, and weaker when the streak could be repaired (Silverman & Barasch 2023)
  • In interviews with 17 runners who ended streaks of 100+ days, some had run injured to keep the streak alive, and some felt a strong sense of loss afterwards (Ingalls 2026)

Rest is part of the program, so this site has no daily streak. The calendar only marks days you trained and never shows a week as missed.

BProbably true

Rewards for showing up lower intrinsic motivation. Positive feedback about competence raises it.

A meta-analysis of 128 experiments found that engagement- and completion-contingent rewards lowered free-choice intrinsic motivation (effect sizes −0.40 and −0.36), while positive feedback raised it (+0.33, Deci 1999). That is why there are no points or badges, and the records page compares you only with your own past.

References

  1. Harkin B et al. 2016. Does monitoring goal progress promote goal attainment? Psychol Bull. PMID 26479070 ↩
  2. Michie S et al. 2009. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. PMID 19916637 ↩
  3. Gollwitzer PM, Sheeran P. 2006. Implementation Intentions and Goal Achievement: A Meta-analysis of Effects and Processes. Adv Exp Soc Psychol 38:69–119. doi:10.1016/S0065-2601(06)38002-1 ↩
  4. Bélanger-Gravel A et al. 2013. A meta-analytic review of the effect of implementation intentions on physical activity. Health Psychol Rev 7:23–54. doi:10.1080/17437199.2011.560095 ↩
  5. Singh B et al. 2024. Time to Form a Habit: A Systematic Review and Meta-Analysis of Health Behaviour Habit Formation. Healthcare (Basel). PMID 39685110 ↩
  6. Kaushal N, Rhodes RE. 2015. Exercise habit formation in new gym members: a longitudinal study. J Behav Med. PMID 25851609 ↩
  7. Gardner B, Lally P, Wardle J. 2012. Making health habitual: the psychology of ‘habit-formation’ and general practice. Br J Gen Pract. PMID 23211256 ↩ ↩
  8. Lally P et al. 2010. How are habits formed: Modelling habit formation in the real world. Eur J Soc Psychol 40:998–1009. doi:10.1002/ejsp.674 ↩
  9. Rhodes RE et al. 2017. Factors associated with participation in resistance training: a systematic review. Br J Sports Med. PMID 28404558 ↩
  10. Rhodes RE, Kates A. 2015. Can the Affective Response to Exercise Predict Future Motives and Physical Activity Behavior? Ann Behav Med. PMID 25921307 ↩
  11. Teixeira PJ et al. 2012. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. PMID 22726453 ↩
  12. Ntoumanis N et al. 2021. A meta-analysis of self-determination theory-informed intervention studies in the health domain. Health Psychol Rev. PMID 31983293 ↩
  13. Hermsen S et al. 2017. Determinants for Sustained Use of an Activity Tracker. JMIR Mhealth Uhealth. PMID 29084709 ↩
  14. Meyerowitz-Katz G et al. 2020. Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease. J Med Internet Res. PMID 32990635 ↩
  15. Steinberg DM et al. 2013. The efficacy of a daily self-weighing weight loss intervention using smart scales and e-mail. Obesity. PMID 23512320 ↩
  16. Steinberg DM et al. 2015. Weighing every day matters: daily weighing improves weight loss and adoption of weight control behaviors. J Acad Nutr Diet. PMID 25683820 ↩
  17. Zheng Y et al. 2015. Self-weighing in weight management: a systematic literature review. Obesity. PMID 25521523 ↩
  18. Epton T et al. 2017. Unique effects of setting goals on behavior change: Systematic review and meta-analysis. J Consult Clin Psychol. PMID 29189034 ↩
  19. McEwan D et al. 2016. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour. Health Psychol Rev. PMID 26445201 ↩
  20. Halperin I et al. 2022. Accuracy in Predicting Repetitions to Task Failure in Resistance Exercise. Sports Med. PMID 34542869 ↩
  21. Zourdos MC et al. 2016. Novel Resistance Training-Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve. J Strength Cond Res. PMID 26049792 ↩
  22. Mazeas A et al. 2022. Evaluating the Effectiveness of Gamification on Physical Activity: Systematic Review and Meta-analysis of Randomized Controlled Trials. J Med Internet Res. PMID 34982715 ↩
  23. Milkman KL et al. 2021. Megastudies improve the impact of applied behavioural science. Nature. PMID 34880497 ↩
  24. Weakley J et al. 2023. The Effect of Feedback on Resistance Training Performance and Adaptations: A Systematic Review and Meta-analysis. Sports Med. PMID 37410360 ↩
  25. Silverman J, Barasch A. 2023. On or Off Track: How (Broken) Streaks Affect Consumer Decisions. J Consum Res 49(6):1095–1117. doi:10.1093/jcr/ucac029 ↩
  26. Ingalls EE et al. 2026. The dark side of streaking: Examining the backfire potential of run streaking in recreational runners who broke a long-term streak. PLoS One. PMID 42172224 ↩
  27. Deci EL, Koestner R, Ryan RM. 1999. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull. PMID 10589297 ↩