Cycle-based training is real science — and also an evolving one. Here's the work that shapes how Lune programs. We'll keep adding papers as the field grows.
Across 78 studies, performance dips during the early follicular phase (your period) are real — but small. What's big is the variation between women. This is why Lune weights your daily recovery score and symptom log alongside cycle phase instead of applying a one-size template. The longer you use the app, the more your programming reflects you specifically.
Read on PubMed →The largest meta-analysis on oral contraceptives and performance found no meaningful gap between pill users and naturally cycling women — and no day-of-the-pill penalty. This is why Lune has a dedicated mode for hormonal birth control: instead of chasing phantom phases, it shifts programming to your daily recovery, symptom, and energy signals — the inputs that actually matter when your natural cycle is suppressed.
Read on PubMed →Same woman, one leg trained in follicular phase, the other in luteal. The follicular-trained leg gained more strength and more muscle diameter. This is the core paper behind Lune's cycle-optimized program: when you upload your plan, the app shifts your heaviest compound lifts toward follicular days because that's where they produce bigger gains — measured on the same body, not a population average.
Read on PubMed →Four months of cycle-periodized resistance training in trained women. Loading hard sessions into the first two weeks of the cycle beat both luteal-concentrated and evenly-spread training for squat strength, jump performance, hamstring torque, and lean leg mass. When Lune re-programs your plan as "cycle-optimized," this is the periodization pattern it applies — not a theory, a four-month real-world result.
Read on PubMed →The most recent critical review of the field. Concludes follicular-phase resistance training likely produces superior strength and mass gains — while naming the methodological gaps the literature still has. Lune sides with the weight of evidence without overclaiming certainty: if your recovery score or symptom log contradicts the phase default on a given day, the app overrides itself.
Read on PubMed →68,758 women studied. Knee ligament laxity spikes during ovulation. Non-contact ACL injuries cluster outside the luteal phase. Oral contraceptives may cut injury risk by ~20%. This is why Lune's ovulatory-phase workouts (days 14–16) automatically add joint-aware warm-ups, dial back max-load plyometrics, and cue extra control work — your knees literally move differently that week.
Read on PubMed →Your body burns fuel differently across the cycle — more fat oxidation and stronger endurance in the mid-luteal phase when the estrogen:progesterone ratio peaks. This is why Lune's fueling and endurance cues shift with your phase, not just your workout. What works pre-session on day 10 isn't what your body wants on day 22.
Read on PubMed →A landmark review with a counterintuitive finding: when methodology is tight, your muscles aren't actually weaker on your period. This is why Lune doesn't skip your training day or blanket-cut your loads just because you're bleeding. It tunes volume, inter-set rest, and perceived-effort targets based on what your body is reporting — not a phantom strength penalty.
Read on PubMed →Low energy availability — eating less than your training demands — suppresses reproductive hormones, accelerates bone loss, and tanks performance. Lune tracks your training load against recovery and symptom signals and flags patterns consistent with underfueling. It won't diagnose, but it will surface what it's seeing so you can decide what to do next.
Read on PubMed →The clinical foundation. Maps how estrogen and progesterone interact with metabolism, thermoregulation, psychology, and injury risk across every cycle phase. The phase ring you see inside Lune — menstrual, follicular, ovulatory, luteal, each with its own energy profile and training implications — traces directly back to papers like this one.
Read on PubMed →This field is still young relative to male-focused sports science. Some findings are robust (early-follicular performance dips are small, ovulatory knee laxity is real); others are active areas of debate. Lune errs toward individual adaptation over rigid templates, and we update our programming as the evidence moves.
Lune turns this work into adaptive, cycle-aware programming — without making you read the papers yourself.
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