Reading the Research as a Female Triathlete: What Transfers, What Doesn't, and Where to Look

The companion article to this one, "Mostly Men, Mostly Cyclists," sets out the problem: women are a minority of sport science participants, only around 6-7% of studies recruit women alone, and many of the small lab studies behind triathlon advice tested eight to fifteen men. This article is about what to do with that. If you're a female triathlete, how should you read a study, a coaching article or a headline, and decide whether it applies to you?

The goal isn't to throw out everything learned from men. That would leave you with very little, and much of it would be fine. It's to sort findings into those you can reasonably borrow, those you should test on yourself first, and those that need more evidence before anyone should act on them.

Three questions to ask of any finding

Before anything else, three quick checks:

1. Who was studied? Number of participants, their sex, their training level, and their age. "Trained triathletes" often turns out to mean all or mostly men. Many studies report this only in the methods section or a table.
2. What exactly was measured? A lab time trial, a heart-rate response, an efficiency number, a race result? A change in a lab measure isn't the same thing as a faster race.
3. How big and how certain was the effect? A statistically significant difference in eight people can be small, fragile or both. A pooled analysis of dozens of studies is more trustworthy than any single small one, especially if the authors grade the quality of the evidence.

None of these questions is specific to women, but each one matters more when the population studied doesn't include you.

A transfer ladder

This framework is TriForward's own way of organising the decision, not a published standard. It puts findings on four rungs, from most to least likely to transfer.

Rung 1: Tested in women, or in both sexes with results reported separately

This is the best case. If a finding has been shown in female athletes, or a mixed study reports women's results separately and they point the same way, you can treat it much like any other well-supported finding. Large race-result analyses often fall here. For example, an analysis of more than 410,000 Ironman results by Käch and colleagues reported age-related decline separately for women and men.

Rung 2: Tested in mixed groups, but not analysed by sex

Many studies include a few women but don't report them separately. The result describes a mostly male average. Reasonable to use, but with a little more caution, especially if women were only a handful of participants.

Rung 3: Men only, but the claim rests on general physiology

The claim depends on mechanisms with no known reason to reverse in women: training a skill makes you better at that skill, sudden spikes in training load raise injury risk, surges cost more energy than steady efforts. These probably transfer, even if the size of the effect might differ. That's an inference, not a finding.

Rung 4: Men only, and the claim depends on something known to differ by sex

This is where caution matters most. If the advice is built on percentages of VO2max, fatigue resistance, specific fuelling doses, body composition or age-related change, it touches areas where differences have been documented. Treat it as a hypothesis for you, not a rule.

Worked example 1: the swim-pacing study

One of the most cited triathlon swim studies is by Peeling and colleagues (2005). Nine highly trained male triathletes completed three lab-based sprint triathlons, swimming the 750 m at 80-85%, 90-95% or 98-102% of their swim time-trial speed, then racing the bike and run flat out. Swimming at 80-85% or 90-95% produced faster bike times than swimming flat out, and 80-85% produced a faster overall time.

Where does this sit on the ladder? The underlying idea, that going into the red early costs you later, is general physiology (rung 3). But the specific percentages are rung 4 territory. They come from nine men, in a pool, in a sprint-distance lab format, and the optimal number could plausibly differ for someone whose physiology, swim ability relative to bike ability, or race distance is different. TriForward's article "Swim Easy, Bike Faster?" explains why the advice is no longer a blanket rule even for the population studied.

How to apply it: keep the principle (don't start the swim at a pace you can't hold) and test the number on yourself. Swim a race-specific set at a few controlled efforts and see how the following bike or run feels, or compare race-simulation sessions. Don't adopt a precise percentage from a nine-man study as your target.

Worked example 2: carbohydrate loading

In 1995, a study by Tarnopolsky and colleagues found that female endurance athletes didn't increase muscle glycogen when carbohydrate went from 58% to 74% of their diet, while men did. For years that was read as "women can't carb-load."

Two 2001 studies reframed it. Tarnopolsky's own group, testing six trained women and six trained men, found women did store more glycogen when total energy intake was also increased. James and colleagues gave women and men the same carbohydrate dose relative to lean body mass and found no sex difference in how much glycogen they stored.

The apparent sex difference came from how the women were fed, mostly lower total energy intake, not from an inability to store glycogen. Two lessons follow. First, a fuelling dose expressed as a percentage of diet, or as a fixed number of grams, can mean very different things for athletes of different sizes and energy intakes. Doses scaled to body size are more transferable. Second, a "sex difference" from a small early study can turn out to be a design artefact. Be as cautious with dramatic female-specific claims as with male-derived ones.

Worked example 3: the menstrual cycle

This is the area most female athletes ask about, and where commercial advice has moved fastest. What does the pooled evidence say?

A 2020 systematic review and meta-analysis by McNulty and colleagues in Sports Medicine combined 78 studies on performance across menstrual cycle phases in naturally menstruating women. On average, performance in the early follicular phase (around menstruation) was trivially lower than in other phases. The effect was very small, and the authors rated the quality of the evidence as low. Their conclusion was that general guidance across all women isn't warranted, and that an individualised approach based on each woman's own response is the better way forward.

A companion meta-analysis led by Elliott-Sale (42 studies, 590 participants) looked at oral contraceptive users. Any group-level performance difference compared with naturally menstruating women was most likely trivial, 83% of the studies were rated moderate, low or very low quality, and the authors concluded the evidence doesn't warrant general guidance on using or avoiding the pill for performance. Performance was consistent across the pill cycle.

This is women-specific research done carefully, and its message is modest: the population average doesn't tell you much, individual variation might. That should make you sceptical of rigid, phase-by-phase training prescriptions sold as "science-based." It shouldn't make you dismiss your own experience. Some women do have symptoms that affect training, and that is worth tracking (see below) and discussing with a clinician if it's significant.

Where to find women-specific insight

There's no single trustworthy source, but some kinds of evidence are much better than others.

• Systematic reviews and meta-analyses of female-only studies. These pool many small studies and usually grade the evidence. The McNulty and Elliott-Sale reviews are good examples. When a review says the evidence quality is low, take that seriously.
• Studies that verify menstrual status. A 2021 methodological guide by Elliott-Sale and colleagues set out how studies with women should define and confirm cycle phases and hormonal status. Studies that follow this kind of standard (hormone measurements, not just self-reported dates) are more trustworthy than those that don't. A 2025 audit found only about 5.6% of sports medicine studies accounted for menstrual status at all, so these are rare and worth looking for.
• Large race-result analyses that report women separately. Datasets of hundreds of thousands of finishers don't explain mechanisms, but they do show real-world patterns for women, such as when performance declines with age or which split predicts finishing time. The 2023 analysis of more than 820,000 age-group Ironman 70.3 finishers by Silva and colleagues, for example, reported that the performance gap between women and men narrowed progressively from age 50.
• Reviews focused on female triathletes. The 2025 narrative review by Loosli and colleagues summarises 147 papers on female versus male triathletes and is a reasonable starting map, while being explicit that many gaps remain.
• Studies where women were trained, not just tested. Many female-specific papers compare women and men in a single lab visit. Fewer follow women through a training programme. For questions like "will this session type make me faster?", intervention studies in women are worth far more than one-off comparisons. They're rarer, so when you find one with a reasonable sample size and a control group, give it extra weight.
• Qualified practitioners with female-athlete experience. Sports physicians, physiotherapists and registered sports dietitians can interpret evidence for your situation in a way no article can, particularly for contraception, heavy periods, pregnancy, return to training after childbirth, and menopause.

Masters women and the menopause gap

If you're over 45, you're in the least-researched group. A 2024 editorial in the British Journal of Sports Medicine by McNulty, Olenick, Moore and Cowley argued that perimenopausal and postmenopausal women are likely a small fraction even of the roughly 6% of studies that recruit women only, and called for a deliberate effort to close the gap.

What can you reasonably assume in the meantime? This is inference, not established research:

• General training principles still apply. Consistency, progressive load and adequate recovery have no known reason to stop working.
• Your own response is the most reliable data you have. If recovery between hard sessions seems to take longer than it used to, that's worth acting on whether or not a study has confirmed it in your age group.
• The age-related swim decline begins earlier than bike or run decline for both sexes, which is one reason that keeping swim frequency and technique work going in later age groups may pay off.
• Menopause symptoms that disrupt sleep, recovery or training are a medical conversation, not something to solve with a training tweak.

Tracking your own response

Since the honest answer to many questions is "it depends on you," it's worth collecting your own data. A simple approach:

• Log cycle day, or pill or no-pill week, alongside key sessions for at least three cycles.
• Record session RPE, how you felt, sleep, and any symptoms. Do this alongside objective numbers like pace and power for repeatable sessions.
• Look for consistent patterns across cycles, not one bad session. A single poor workout in any phase is normal variation.
• If you see a consistent pattern, plan around it modestly, for example by moving a key session by a day or two, rather than restructuring your whole block.

The same approach works for other questions the research can't yet answer for you: whether a fuelling strategy sits well in your gut on long sessions, how long you need between hard swim or run sets, or whether a swim pacing strategy leaves you better placed for the bike. Change one thing at a time and keep the comparison sessions as similar as possible, so you can tell a real effect from an ordinary good or bad day.

This doesn't replace research, but it is exactly the individualised approach the researchers themselves recommend.

Red flags in women-specific advice

Female-specific content is growing fast, and not all of it is better than the male-derived advice it replaces. Be wary when you see:

• Precise phase-by-phase prescriptions presented as settled science, when pooled evidence shows trivial average effects and low-quality data.
• Claims with no sample size, no study, or a single small study, especially if they require buying something.
• Advice that encourages eating less in any phase, or treats missed periods as normal for athletes.
• "Women are just small men" or "women are completely different" framing. The evidence supports neither extreme.

The aim is the same standard you'd apply to any research: who was studied, what was measured, how big and how certain the effect was. Female athletes just have more reason than most to ask.

References

• Cowley ES, Olenick AA, McNulty KL, Ross EZ. "Invisible Sportswomen": The Sex Data Gap in Sport and Exercise Science Research. Women in Sport and Physical Activity Journal, 2021;29(2):146. doi:10.1123/wspaj.2021-0028
• Ose BM, Eisenhauer J, Roepe IG, Herda AA, Vopat BG, Vopat L. Where Are All the Female Participants in Sports and Exercise Medicine Research? A Decade Later. American Journal of Sports Medicine, 2025. doi:10.1177/03635465241278350
• Peeling P, Bishop D, Landers GJ. Effect of swimming intensity on subsequent cycling and overall triathlon performance. British Journal of Sports Medicine, 2005;39(12):960–964. doi:10.1136/bjsm.2005.020370
• Tarnopolsky MA, Zawada C, Richmond LB, Carter S, Shearer J, Graham T, Phillips SM. Gender differences in carbohydrate loading are related to energy intake. Journal of Applied Physiology, 2001;91(1):225–230. doi:10.1152/jappl.2001.91.1.225
• James AP, Lorraine M, Cullen D, Goodman C, Dawson B, Palmer TN, Fournier PA. Muscle glycogen supercompensation: absence of a gender-related difference. European Journal of Applied Physiology, 2001. doi:10.1007/s004210100499
• McNulty KL, Elliott-Sale KJ, Dolan E, Swinton PA, Ansdell P, Goodall S, Thomas K, Hicks KM. The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta-Analysis. Sports Medicine, 2020;50(10):1813–1827. doi:10.1007/s40279-020-01319-3
• Elliott-Sale KJ, McNulty KL, Ansdell P, Goodall S, Hicks KM, Thomas K, Swinton PA, Dolan E. The Effects of Oral Contraceptives on Exercise Performance in Women: A Systematic Review and Meta-analysis. Sports Medicine, 2020;50(10):1785–1812. doi:10.1007/s40279-020-01317-5
• Elliott-Sale KJ, Minahan CL, Janse de Jonge XAK, Ackerman KE, Sipilä S, Constantini NW, Lebrun CM, Hackney AC, et al. Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants. Sports Medicine, 2021;51(5):843–861. doi:10.1007/s40279-021-01435-8
• Silva C, Nikolaidis PT, Valero D, Weiss K, Villiger E, Thuany M, Andrade M, Knechtle B. Predicting overall performance in Ironman 70.3 age group triathletes through split disciplines. Scientific Reports, 2023;13:11492. Loyola Marymount University repository record
• Loosli M, Nikolaidis PT, Scheer V, Wilhelm M, Forte P, Andrade M, Rosemann T, Duric S, Cuk I, Knechtle B. Women in the triathlon: the differences between female and male triathletes, a narrative review. Frontiers in Sports and Active Living, 2025. doi:10.3389/fspor.2025.1567676
• McNulty K, Olenick A, Moore S, Cowley E. Invisibility of female participants in midlife and beyond in sport and exercise science research: a call to action. British Journal of Sports Medicine, 2024;58(4):180. doi:10.1136/bjsports-2023-107165
• Käch IW, Rüst CA, Nikolaidis PT, Rosemann T, Knechtle B. The age-related performance decline in Ironman triathlon starts earlier in swimming than in cycling and running. Journal of Strength and Conditioning Research, 2018;32(2):379–395. doi:10.1519/JSC.0000000000001796