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Aypex

Performance Health

How well the parts of your life are working together to help you perform today and build the capacity to handle tomorrow.

A hard day, or a real change in what you can carry.

State is what’s available to you right now. It moves quickly, with sleep, stress, recovery, pain, mood, and whatever the day is demanding.

Capacity is the broader range you can operate within. It moves slowly, through training, learning, recovery, repeated strain, health and life experience.

Potential is how that capacity could develop over time.

The useful thing is being able to tell the two apart.

CapacityStatePotentialNOW

The same output can cost two people entirely different things.

Output is the visible part. It says nothing about what producing it took, or whether that is sustainable.

Two people can hold the same line for months while one is adapting to it and the other is drawing down on a reserve that isn’t being replaced.

Performance health concept
Visible outputWhat it costs underneathAccumulatingSustainedTime

Demand becomes capacity, or it becomes cost.

Every demand produces a response, and every response carries a cost. What happens next depends on recovery.

Enough of it, repeatedly, and the range widens. Not enough, repeatedly, and it narrows — usually long before anything announces itself.

  1. Demand
  2. Response
  3. Cost
  4. Recovery
  5. AdaptationRaises what you can meet next time
  6. DegradationWhere the loop leaks

What we can show, and what we can’t yet.

Evidence for an approach is not evidence for a product. These are kept separate on purpose, and each tier is marked with where it honestly stands.

The work below establishes how these parts of a life move performance. None of it is a finding about Aypex.

Complete

Established literature

Peer-reviewed work on how the parts of a life move performance. These are findings about people in general — how sleep, stress, relationships and engagement show up in what someone can actually produce. None of them is a finding about Aypex.

+9%

More sleep made measurably better athletes.

Eleven Stanford varsity basketball players extended nightly sleep for five to seven weeks, gaining 110.9 ± 79.7 minutes over baseline. Sprint times improved from 16.2 ± 0.61 to 15.5 ± 0.54 seconds. Free-throw accuracy rose 9% and three-point accuracy 9.2%. Reaction time improved, vigour rose and fatigue fell.

A small sample, and the clearest demonstration that a life input moves an output people already measure. This is why sleep sits at the centre of the picture rather than in a separate health column.

Mah, C. D., Mah, K. E., Kezirian, E. J., & Dement, W. C. (2011). The effects of sleep extension on the athletic performance of collegiate basketball players. Sleep, 34(7), 943–950.

ES = .655

How a team gets on predicts how it performs.

Forty-six studies containing 164 effect sizes. A significant moderate-to-large cohesion–performance relationship across sport teams (ES = .655), holding at ES = .499 in the subset using the Group Environment Questionnaire. The effect was larger for female teams (ES = .949) than male (ES = .556), and present whether cohesion was treated as cause or consequence of success.

This is what licenses putting relationships beside sleep and training rather than in a separate emotional category. They are a performance variable, measured as one.

Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport & Exercise Psychology, 24(2), 168–188.

ρ = .43

What someone brings to the work shows up in the work.

A meta-analytic review found work engagement positively related to task performance (Mρ = .43) and contextual performance (Mρ = .34), and still predictive when rated by someone other than the person themselves (ρ = .39). Engagement retained criterion-related validity over job attitudes rather than restating them.

The same idea as purpose, measured against output instead of health outcomes. It is why meaning belongs in the picture for people whose performance is work rather than sport.

Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology, 64(1), 89–136.

r = 0.27

Psychological load shows up in whether you are available at all.

Two meta-analyses across 48 published studies and 161 effect sizes. Stress responses showed the strongest association with injury rates (r = 0.27, 80% CI 0.20–0.33); history of stressors was weaker at r = 0.13. Every one of the seven intervention studies included reduced injury rates in the treatment group.

Availability is the performance variable underneath every other one — you cannot train or compete if you are not on the field. The intervention studies are the part that matters: this is addressable, not fixed.

Ivarsson, A., Johnson, U., Andersen, M. B., Tranaeus, U., Stenling, A., & Lindwall, M. (2017). Psychosocial factors and sport injuries: Meta-analyses for prediction and prevention. Sports Medicine, 47(2), 353–365.

Mood moves before performance does.

Nineteen studies, 143 study coefficients, total n = 1,932. Mood was more affected by sleep loss than either cognitive or motor performance — it moved first, and it moved further. Partial sleep loss affected functioning more than either long-term or short-term total deprivation.

The reason a read is worth having early. How someone feels shifts while their output still looks fine, which is exactly the window where knowing is useful.

Pilcher, J. J., & Huffcutt, A. I. (1996). Effects of sleep deprivation on performance: A meta-analysis. Sleep, 19(4), 318–326.

In progress

Emerging, with limits stated

The language argument lives here, and it is presented with its ceiling visible.

5.1% vs 38.5%

Language carries psychological signal, and the effect depends heavily on method.

Across 31 samples (n = 85,724), self-reported personality traits correlated with linguistic categories, but effect sizes were small — the strongest ranged from |ρ| = .08 to .14, with 52 word-count categories explaining about 5.1% of personality variance on average. Observer-reported traits did substantially better, |ρ| = .18 to .39, explaining 38.5% of variance.

We cite this rather than hide it, because the distinction argues for how Aypex is built rather than against it. That work tests word-frequency counting against stable personality traits, between people. Aypex does none of those three things: it reads meaning rather than counting words, it tracks state rather than trait, and it compares a person against their own history rather than against a population.

Boyd, R. L., et al. (2022). The kernel of truth in text-based personality assessment: A meta-analysis of the relations between the Big Five and the Linguistic Inquiry and Word Count (LIWC). Psychological Bulletin, 148(11–12), 843–868.

90.7–94.8%

The personal baseline is the part with the best support.

An ecological momentary assessment study analysed 7,680 text responses from 97 adolescents, comparing group-level and individualised models for predicting daily negative affect from text features. Individualised models matched group models on variance explained but produced lower prediction error. With the best model selected per person, predicted and observed emotion scores correlated significantly for 90.7% to 94.8% of participants.

This is the support for reading someone against their own history rather than a population average. It is the closest thing on this page to external backing for a design decision rather than for a premise.

Ecological momentary assessment study, 2025. PMC12047991.

Not started

Aypex's own validation

No instrument validation. No outcomes research. No published work.

This tier is empty, and it stays visible. Nothing here has been measured yet, and saying so plainly costs less than papering over it.

Two ways in.

The Aypex AppThe Aypex Hub