Injuries, Rehabilitation, Scientific publication, Sports Science

High-volume injections for Achilles tendinopathy: no difference from sham at 12 months

Achilles tendinopathy is one of those conditions every sports medicine team knows too well. Around 6% of people will have it at some point in their lives, 38% of workers affected report reduced work capacity, and exercise-based rehabilitation, the recommended first-line treatment, does not resolve the problem in up to 44% of cases. While I personally solved such issues always with resistance exercise, almost half of those with persistent symptoms go on to seek other treatments, and injections are a common next step. One of the most popular over the last 15 years has been the high-volume injection: 50 mL of fluid pushed in between the tendon and the fat pad that sits in front of it, often with a corticosteroid added.

A randomised controlled trial just published in The BMJ by Peter Malliaras and colleagues, the Inject trial (28 October 2026), gives us the best data so far on whether these injections work. The headline is clear: at 12 months there was no difference between treatments. Neither the high-volume injection with corticosteroid nor the one without it improved pain and disability compared with a sham injection, and the two injections did not differ from each other.

What the trial did

The team randomised 192 adults with midportion Achilles tendinopathy at a radiology clinic in Melbourne between December 2019 and July 2022, recruited mainly through paid social media advertising and referrals from local doctors and physiotherapists. These were not mild cases: mean age 49, 55% men, a median symptom duration of two years, a baseline VISA-A score of 48.7 out of 100, about half with symptoms in both legs and a third with obesity. Everyone had pain for at least three months, ultrasound evidence of tendon pathology (hypoechoic areas and/or Doppler signal), and had not responded to previous treatment; almost 80% had already tried exercise.

Participants were randomised 1:1:1 to one of three arms:

  • Sham injection (n = 64)
  • High-volume injection without corticosteroid (n = 63)
  • High-volume injection with corticosteroid (n = 65)

Crucially, everyone then received the same care from a physiotherapist: a progressive 12-week calf and Achilles loading programme, education on activity modification using a pain-monitoring model, and information about the condition and its prognosis. So the question the trial answers is not “injection versus nothing” but “does adding an injection to good rehabilitation make a difference?”. Adherence was high, with 91–95% of participants averaging at least two exercise sessions a week across groups.

The primary outcome was the VISA-A (Victorian Institute of Sport Assessment – Achilles, 0–100, higher is better) at 52 weeks, and the trial was powered to detect a 10-point difference between any two groups. Because there were three pairwise comparisons, the authors used 98.33% confidence intervals and a significance threshold of 0.0167. A full cost-utility analysis from a societal perspective ran alongside, and the statistical analysis plan was published before any analysis started.

The injections, explained

The high-volume injection was introduced by Otto Chan and colleagues in 2008. Its rationale is that part of the pain in Achilles tendinopathy comes from new blood vessels and nerves growing into the front of the tendon from Kager’s fat pad, the triangle of fat between the Achilles, the deep flexor tendons and the heel bone. Injecting a large volume of fluid exactly at that interface is thought to disrupt this neurovascular ingrowth mechanically or by temporarily reducing blood supply, while a corticosteroid, when added, provides an anti-inflammatory effect.

Each participant received a single injection, under ultrasound guidance, from one of three experienced musculoskeletal radiologists, with the patient lying face down. Dr Chan himself travelled to Melbourne to train the radiologists, and 11% of injections were randomly observed to check fidelity. In practice:

  • High-volume injection with corticosteroid: five 10 mL syringes connected in sequence through a tube. The first contained 10 mL of 0.5% bupivacaine (a local anaesthetic) with 25 mg of hydrocortisone, followed by four syringes of 10 mL normal saline, for a total of 50 mL. The needle was placed at the interface between Kager’s fat pad and the tendon and moved gently along it, under continuous ultrasound monitoring, to spread the fluid evenly over the pathological area.
  • High-volume injection without corticosteroid: identical, except the first syringe contained only the 10 mL of bupivacaine.
  • Sham injection: 2–3 mL of 0.5% bupivacaine, injected at least 10 mm anterior to (deeper than) the fat pad–tendon interface, a placement unlikely to be therapeutic. It produces a numbing effect similar to the high-volume injections.

The masking procedures are worth noting. Every participant went through an identical procedure with five 10 mL syringes. In the sham group, a three-way stopcock diverted the fluid to a hidden bag. The syringes were taped so the colour of the steroid could not be seen, and afterwards participants wore a sleeve over the Achilles to hide any swelling from the large volume. The radiologists knew whether they were giving an active or a sham injection, but not whether the active one contained steroid. The check worked: immediately after the injection, 67–73% of participants in each group were unsure what they had received, and only 22–24% in each group believed they had received an active injection.

Schematic of the high-volume and sham injections used in the Inject trial: side view and ultrasound cross-section of the Achilles tendon and Kager's fat pad, needle position, and the contents of the five syringes
How the injections were delivered: side view, ultrasound cross-section and syringe contents (schematic; click to enlarge).

What each ingredient does

Bupivacaine is a long-acting local anaesthetic. It blocks voltage-gated sodium channels in nerve fibres, so pain signals cannot be transmitted. Its peak effect comes after about 30–45 minutes and the numbness lasts hours, not weeks. In the trial it gave the same numbing sensation in all three arms, which is part of what kept participants unaware of their group. The 50 mg used in the high-volume injections is well below the usual maximum dose of about 2 mg/kg (up to 175 mg), and the sham delivered 10–15 mg.

Hydrocortisone is a corticosteroid. It diffuses into cells, binds the glucocorticoid receptor and switches genes off and on in the nucleus: it dampens the NF-κB-driven production of inflammatory cytokines and COX-2, and increases annexin A1, which blocks phospholipase A2 and therefore the production of prostaglandins and leukotrienes. The net result is less inflammation, less vascular leakage and less pain sensitisation. The same actions have a cost in tendon tissue: injected corticosteroids reduce local collagen production, which can limit healing and leave the tendon more prone to failure. Hydrocortisone is short-acting and the least potent of the injectable steroids: 25 mg is equivalent to about 5 mg of triamcinolone, compared with the 40 mg typically injected into a large joint. Because tendinopathy is thought to be more a failed healing response than classic inflammation, damping that response can ease pain early without rebuilding the tendon.

Saline provides the volume: 40 mL meant to separate the tendon from the fat pad and disrupt the neovessels and nerves, mechanically or by cutting their blood supply. In Inject, the steroid-free injection made no difference to symptoms, Doppler signal or tendon thickness, so the volume on its own seems to be inactive.

Infographic on how bupivacaine, hydrocortisone and saline in a high-volume injection work, with the benefits, local and systemic side effects, groups needing extra care and contraindications for corticosteroid injections
How the drugs in a high-volume injection work, and the benefits and risks of corticosteroid injections (click to enlarge).

What they found: no difference between treatments

Follow-up was excellent, with VISA-A data from 181 of 192 participants (94%) at 12 months. At the primary endpoint there was no difference between any of the groups:

  • High-volume injection with corticosteroid vs sham: −3.5 points (98.33% CI −12.0 to 5.0)
  • High-volume injection without corticosteroid vs sham: +4.0 points (−4.2 to 12.3)
  • With vs without corticosteroid: −7.5 points (−15.3 to 0.3)

The high-volume injection without steroid showed no benefit over sham at any time point, on VISA-A or on any secondary outcome. Ultrasound told the same story: there were no differences in Doppler signal or tendon thickness between this injection and sham. As the authors put it, simply injecting a volume of fluid does not seem to affect tendon pathology or improve clinical outcomes.

The secondary time points showed a pattern worth knowing about, but it needs to be read with caution. With the steroid injection there was a small benefit over sham at six weeks (+9.2 points, 1.5 to 16.9), together with a better global rating of change. By 26 weeks the steroid group was doing worse than the group that had received the same injection without steroid (−8.6 points, −16.4 to −0.7), with higher worst pain over the previous week (11.3 points on a 0–100 scale). All of these differences were smaller than the 10-point target, and the authors note that the early benefit is unlikely to be clinically meaningful and might be no more than chance, given the number of outcomes and time points tested.

Forest plot of between-group differences in VISA-A at 6, 26 and 52 weeks in the Inject trial, showing no difference between treatments at 12 months
Between-group differences in VISA-A with 98.33% confidence intervals.

Benefits and risks of these injections

Benefits. The case for a corticosteroid-containing injection rests on short-term relief. In the Inject trial this was a 9.2-point VISA-A advantage over sham at six weeks, smaller than the 10-point target and gone by 12 months, along with less time off work early on and lower costs over the year. The procedure is quick, done in one visit and image-guided. The steroid-free injection showed no benefit at any point.

Risks in the trial. There were no Achilles tendon ruptures. Six participants had serious adverse events (two in each group), none related to the trial. Non-serious adverse events were common in all groups (53% sham, 38% without steroid, 49% with steroid), mostly aggravated Achilles pain (28%, 16% and 25%) or other musculoskeletal pain. Only one, injection-site pain after the steroid injection, was judged to be related to the injection itself. The main harm signal was clinical rather than a complication: worse VISA-A and worse pain at 26 weeks with the steroid than without it.

Risks of corticosteroid injections in general. A 2024 review in the American Journal of Roentgenology by Kamel, Rosas and Gorbachova sets out the side effects patients should be told about before a musculoskeletal steroid injection. It also notes that systematic reviews are inconclusive on long-term benefit for joints and tendinopathy, which fits the Inject results. Most of the frequencies below come from mixed injection sites, often joints, rather than from Achilles injections specifically:

  • Local: a post-injection flare of pain is common, reported in 33–35% of hand, wrist and shoulder injections, usually starting 1–2 days later and lasting about 2 days. Skin thinning or loss of colour occurs in up to 10% after superficial injections (for example of the plantar fascia), appears within months and usually resolves within a year. Tendon rupture is rare, about 0.1%, typically between 2 days and 6 weeks after the injection, and the Achilles is one of the tendons involved in case reports. Being next to the tendon rather than in it is not a guarantee of safety: in a rabbit study, injection into the retrocalcaneal bursa weakened the Achilles as much as injection into the tendon. Infection after a joint injection is very rare (under 0.001%), and soft-tissue calcification, faster progression of osteoarthritis and bone injury have also been reported, mainly after joint injections.
  • Systemic: part of the injected steroid reaches the bloodstream. Cortisol production is suppressed for at least a week after a single injection, and for up to 2 weeks in a study of young athletes. Facial flushing affects about 40% and settles within 3 days. Blood pressure and blood glucose can rise for more than a week. About half of premenopausal women have early or delayed periods afterwards. Allergic reactions (0.3–0.5%), raised eye pressure, insomnia, headache and, with repeated use, bone loss are also described.
  • People with diabetes: glucose typically peaks 1–3 days after the injection, and levels up to 500 mg/dL have been reported. The review cites a recommendation to check glucose twice a day for a week, seek help above 360 mg/dL, and avoid injecting if fasting glucose is above 200–250 mg/dL.
  • Surgery: injections are linked to more post-operative infections and to more revision surgery after tendon repair. The review suggests delaying surgery for 1–3 months after an injection and withholding steroid for 3–6 months before a tendon repair or joint replacement.
  • When not to inject: absolute contraindications are infection at the needle site, septic arthritis or osteomyelitis, fracture and a prosthetic joint. Relative ones include the HIV drug ritonavir, surgery planned within 4 weeks, previous blurred vision with steroids, very high blood glucose and a previous allergic reaction. There are still no evidence-based limits on how often or how many times these injections can be given; specialty societies suggest at least 3 months between injections, and some authors suggest no more than 3–5 at a single site.
  • Athletes: an International Olympic Committee consensus statement says corticosteroid injections are not indicated for same-day return to play or within 72 hours of competition, because of the risk of tendon weakening, and the review advises avoiding vigorous activity for at least 48 hours after an injection, partly because of the temporary adrenal suppression. Under the WADA rules, glucocorticoids given by any injectable route are prohibited in competition and washout periods apply, so athletes need to check timing with their anti-doping organisation and, where needed, apply for a Therapeutic Use Exemption.

The local anaesthetic is not entirely innocent either. Its toxicity to cartilage depends on dose and exposure time, it appears to add to the toxicity of the steroid when the two are combined, and animal studies suggest a similar additive effect on tendon cells. A systematic review cited by the AJR authors found bupivacaine at 0.5% or higher, the concentration used in the Inject trial, to be the most toxic to human cartilage in the lab, although what this means at the doses used in a single injection is still poorly understood. Accidental injection into a blood vessel can also cause systemic toxicity, one more reason these procedures are done under image guidance.

The cost paradox

The health economic analysis adds a twist. Over a year, the steroid injection was less costly than sham from a societal perspective, with similar quality-adjusted life years, and had a more than 90% probability of being cost-effective at willingness-to-pay thresholds of AUD 50,000 and 100,000. The saving came from less time off work in the first 12 weeks and lower healthcare costs in the last six months. The injection without steroid had a low probability of being cost-effective (under 60%). The authors are cautious (and rightly so!), though: the QALY differences were very small and the health benefit negligible, so this is a modest economic signal rather than a clinical argument for the injection.

How I read it

For me, this is a very well-designed trial and an important one. A few things stand out.

The primary answer is no difference. Adding a high-volume injection, with or without steroid, to a good rehabilitation programme did not change pain and function at one year. That is the result that should guide practice, and the secondary findings do not change it.

The volume is not the active ingredient. The steroid-free injection tests the mechanical “break the neovessels” idea on its own, and it did nothing measurable to symptoms or to the tendon on ultrasound. This agrees with the 2020 BMJ trial by van der Vlist and colleagues, which also found no benefit of the steroid-free injection over sham up to 26 weeks; Inject extends that to 12 months. Earlier positive results came from much smaller trials by Boesen and colleagues (2017 and 2019); for the 2019 trial of 28 people, the Inject authors point out that the small sample raises concerns about overestimated effects and that using saline as the sham may have compromised masking.

Any steroid effect follows a familiar pattern. A possible small, short-lived benefit followed by worse outcomes later is what we have seen with corticosteroid injections in other tendinopathies, particularly lateral elbow. Here it was seen with a low dose (25 mg hydrocortisone) delivered around, not into, the tendon. In his accompanying editorial, John Orchard (University of Sydney) makes the practical point that corticosteroids are neither uniformly good nor uniformly bad: their use depends on whether short-term needs outweigh longer-term ones, patients should never be told an injection will “fix” tendinopathy, and clinicians need to explain that trade-off. The authors give a similar example: a self-employed person under financial pressure may reasonably value a few pain-free weeks.

The authors’ own prior beliefs make the result more convincing. One author is a radiologist working in a clinic that offers high-volume injections, and two physiotherapist authors, including the lead author, declare that they have recommended the injection in the past. Running a trial with a well-designed sham, publishing the analysis plan first, sharing the data and code openly, and then reporting a null result is exactly how it should be done, so kudos to the authors.

Rehabilitation that includes resistance exercise remains the backbone. Every participant received the same structured loading programme and education. The time and effort should go into well-dosed, progressive tendon loading, load management and patient education rather than into injections.

Limitations worth keeping in mind

  • It is a single-centre trial and participants were recruited mainly through social media, so they may not represent people seeking care in hospital or clinic settings.
  • The nurse and radiologists delivering the injections could not be masked, although they followed a scripted protocol to limit any influence.
  • With many outcomes and time points, some of the secondary findings could reflect chance rather than true effects.
  • Only the VISA-A was used from the four instruments in the newer core outcome set for Achilles tendinopathy, COVID-19 restrictions increased missing imaging and strength data, and adverse events and costs were self-reported every six weeks, which may be subject to recall bias.
  • Half the participants had symptoms on both sides but only the worse side was injected.
  • The population was middle-aged with long-standing symptoms. Whether the results apply equally to younger, high-level athletes with a shorter history is not known.

Bottom line

There was no difference between treatments at 12 months: high-volume injections, with or without corticosteroid, are no better than a sham injection when added to good rehabilitation for midportion Achilles tendinopathy. The steroid version may give a small, short-lived benefit at six weeks but is followed by worse outcomes than the steroid-free injection at six months. In the authors’ words, the evidence “should prompt clinicians to reconsider the rationale for using these injections in people with Achilles tendinopathy”, and the clinical data do not support routine use.

Infographic summarising the Inject trial: no difference between treatments at 12 months, with participants, the three injection arms, secondary findings, costs and safety
The Inject trial at a glance (click to enlarge).

Reference: Malliaras P, Connell D, Boesen AP, Kearney RS, Menz HB, Morrissey D, Munteanu SE, Silbernagel KG, Underwood M, Forbes A, Haines TP. Efficacy and cost effectiveness of high volume injections with and without corticosteroid compared with sham for midportion Achilles tendinopathy (Inject trial): randomised controlled trial. BMJ 2026;395:e100807. https://doi.org/10.1136/bmj-2026-100807 (open access). Data and code: https://doi.org/10.26180/c.8671176.

Side effects review: Kamel SI, Rosas HG, Gorbachova T. Local and systemic side effects of corticosteroid injections for musculoskeletal indications. AJR 2024;222:e2330458. https://doi.org/10.2214/AJR.23.30458

Trial protocol: Malliaras P, et al. BMJ Open Sport & Exercise Medicine 2021;7:e001136. https://doi.org/10.1136/bmjsem-2021-001136. Trial registration: ACTRN12619001455156.

Laboratory balance beneath a glowing data network and warning symbol
Activity Monitor, Artificial Intelligence, monitoring, Physical Activity

Your data, your prescription? What Google’s personal health agent tells us about AI-driven activity prescription

For more than a decade we have been collecting data from wearables at an incredible rate: steps, heart rate, heart rate variability, sleep, GPS traces, training sessions. Yet the step that matters most has always been the hardest one: going from numbers to a decision. What should this person do next week, given their data, their health history and their life? In high-performance sport we solve that problem with teams of people. For the general population, that team simply does not exist.

A paper just published in Nature by Heydari and colleagues at Google Research, “The anatomy of a personal health agent” (21 October 2026), is the most serious attempt I have seen so far to build that team in software. It is worth reading carefully, both for what it shows and for what it does not show, because it gives us a realistic preview of AI-generated, personalised activity prescriptions based on our own data.

What they built

The authors started from users rather than from technology. They analysed 1,370 real consumer health queries from search, Gemini and the Fitbit forum, surveyed 555 people using an experimental Fitbit Labs chatbot and ran a workshop with 14 experts. Four types of need emerged: general health knowledge, interpretation of personal data, wellness advice, and symptom assessment. These needs require very different skills — numerical precision and code for data questions, factual grounding for knowledge and symptoms, empathetic open-ended dialogue for coaching — and the team found that a single large language model struggled to balance them.

Their solution is a modular, multi-agent system — the Personal Health Agent (PHA) — built on Gemini 2.0, made of three specialists coordinated by an orchestrator:

  • A data science agent that turns a vague question (“am I getting fitter?”) into an explicit analysis plan, then writes and runs Python code on the user’s wearable time series, including comparisons with population percentiles by age and sex.
  • A domain expert agent that reasons over wearable data, blood tests and health history, using tools such as NCBI literature search, web search and population statistics to ground its answers.
  • A health coach agent built around motivational interviewing: it identifies goals first, explores constraints, asks for feedback and only then offers a SMART recommendation.

The orchestrator decides which agent leads and which ones support, rewrites a targeted sub-question for each, checks the combined answer for coherence (and to avoid asking the user for data it already has), and keeps a memory of goals, barriers and preferences across turns.

What the evaluation shows

The evaluation is large: 11 benchmark tasks, more than 7,000 annotations and about 1,350 hours of work from end users and experts, grounded in the WEAR-ME study (1,165 Fitbit users with blood tests and questionnaires). A few results stood out for me.

Data analysis. The data science agent’s analysis plans scored 75.6% against 53.7% for the base model. The most striking gain was in checking whether there was enough data to answer the question in the first place: 66.5% of plans did so, against just 5.3% for the base model. Anyone who has worked with wearable data knows that missing days, non-wear periods and artefacts are where most bad conclusions come from. Its code passed 75.5% of expert-written unit tests at the first attempt (58.4% for the base model), rising to 79% with self-correction.

Health knowledge and context. On more than 2,000 board-style questions in endocrinology, cardiology, sleep medicine and strength and conditioning, the gain over the base model was small (83.6% vs 81.8%). Much bigger differences appeared when personal context mattered: end users rated 96.9% of the domain expert’s personalised answers as trustworthy against 38.7% for the base model, and five clinicians judged 65% of its multimodal health summaries useful against 30%.

Coaching. Users preferred the coach agent on five of six coaching themes, and expert coaches preferred it on all six. Interestingly, both groups gave the plain baseline a slight edge on progress measurement — a weakness the authors then addressed in the integrated system.

The integrated agent. In live multi-turn conversations, 45 users role-playing real WEAR-ME profiles ranked the orchestrated PHA first 46.7% of the time, against 28.9% for a parallel multi-agent design and 24.4% for a single agent. The experts were far more decisive: the PHA was ranked best in 66.7% of conversations, roughly five times as often as the single agent, with an overall quality score of 72.8% against 64.3% and 54.4%.

Why this matters for activity prescription

What struck me most is that the architecture mirrors how a good performance support team works. Someone looks at the data properly, someone interprets it in the context of the person’s health and history, and someone turns it into a plan the person will actually follow. Exercise prescription needs all three. Frequency, intensity, time and type are not chosen from a guideline table alone; they depend on where the person is starting from, what they can safely tolerate, and what they are willing and able to do.

Seen through that lens, the near-future pipeline is easy to imagine:

  1. Baseline from your own data: real habitual steps, active minutes, resting heart rate, HRV and sleep — computed, not guessed, and checked for missing data.
  2. Comparison and context: where you sit relative to people of your age and sex, and what your blood markers and conditions mean for what is safe.
  3. A negotiated dose: a first step that fits your constraints and motivation rather than a generic “150 minutes a week”.
  4. Progression: memory of what was agreed and what actually happened, so the next prescription builds on the last one.

The example conversation in the paper’s extended data shows this already. A user with type 2 diabetes, a BMI of about 36, around 5,500 steps per day and a recent hospital admission asks how to get back to 10,000 steps. Instead of prescribing the target, the agent explores why it matters to her, picks up symptoms that suggest possible sleep apnoea, recommends speaking to her doctor, and agrees on several short 10-minute walks per day as a starting point. That is, in essence, a graded prescription with a red-flag screen built in. Another example I liked: asked whether more activity leads to more deep sleep, the data science agent reported a correlation of about 0.14 and said plainly that the relationship was weak — exactly the kind of honest answer a good analyst gives and a lot of consumer apps do not.

The real opportunity is scale. One practitioner cannot review thousands of people’s data every day and adjust their plan. A system like this could make the first layer of individualised prescription and follow-up available to anyone wearing a device, and free human experts to focus on the people and decisions that need them.

The risks we should not gloss over

The authors are refreshingly upfront about limitations, and they describe the PHA as a research prototype rather than a clinical tool. Still, for anyone tempted to treat this as a ready-made prescription engine, these are the issues I would keep in mind.

  • Safety at scale. In the expert safety review of the clinical summaries, 83% carried no or minimal potential for harm, but 15% were judged mildly harmful and 2% moderately to severely harmful. Two per cent sounds small until it is multiplied by millions of users.
  • No outcome data. Everything measured is conversation quality and short-term task performance. There is no evidence yet that people change behaviour, get fitter or become healthier. For an activity prescription, the only test that ultimately matters is the dose–response over weeks and months.
  • Data quality and generalisability. The data come from one device ecosystem (Fitbit), and consumer estimates of sleep stages or HRV have their own validity limits. Even the improved agent checked data sufficiency in only two-thirds of its plans, so one-third did not.
  • Simulated users. In the live studies, participants role-played personas matched to their own profiles rather than seeking help for themselves, and parts of the evaluation relied on LLM-based autoraters. Both are reasonable research choices, but neither is the real world.
  • Training load is not in scope. The system was not evaluated on periodisation, load management, injury risk, environmental stress such as heat, or athletic populations. Extending it to performance settings needs its own validation.
  • Evidence quality. Open-web retrieval can surface weak or conflicting evidence. The authors themselves suggest restricting retrieval to curated sources and showing provenance next to every recommendation.
  • Privacy, equity and cost. Real deployment needs proper privacy and security controls, fairness checks across populations, languages and literacy levels, and a cheaper architecture: the PHA averaged 6.5 model calls and about 205 seconds per response, against 3 calls and 36 seconds for a single agent.
  • Who built and judged it. The work was funded by Alphabet, most authors are Google employees, and the system runs on Google models and Fitbit data. That does not invalidate the findings — and releasing the code and benchmark is a genuinely good step — but independent replication will matter.
  • Coaching versus prescribing. The coach agent sometimes declines to give a concrete number and steers the user to decide for themselves. Good motivational practice, but people asking for an exercise prescription often need a clear, specific dose — and someone accountable for it.

What I would want to see next

Before trusting an AI-generated activity prescription, I would want to know four things: whether it checked the quality of the data before drawing conclusions, whether its evidence comes from curated sources it shows me, whether it recognises red flags and hands off to a clinician rather than reassuring, and whether its prescriptions are reviewed against what actually changed over time. The next generation of studies should be prospective, longitudinal and outcome-based, with people using the system for themselves, across devices and populations, and with clinicians and coaches in the loop.

Bottom line

This paper does not show that AI can prescribe exercise better than a qualified professional. It shows something more modest and, I think, more important: that splitting the job into data analysis, domain expertise and coaching, and orchestrating them carefully, produces answers that experts judge to be substantially better than a single general model. Personalised activity prescriptions built from our own data are no longer a distant idea. The challenge now is to prove that they are safe and that they actually change behaviour and health — and to make sure the humans who are accountable for those decisions stay firmly in the loop.

Reference: Heydari, A. A. et al. The anatomy of a personal health agent. Nature (2026). https://doi.org/10.1038/s41586-026-11161-8 (open access). Code: Google-Health/consumer-health-research.

Data Analytics, Sports Science

Kona 2026: Heat Decided the Titles, Shoes Make the Times, and Cooling Is the Next Frontier

A week ago I published a preview of the IRONMAN World Championship built around my dashboard, the newest carbohydrate research and the evidence on heat acclimatisation. I ended that post by saying that in Kona the athlete leading with 10 km to go usually wins, unless the heat decides otherwise. On 10 October the heat decided otherwise in both races.

In this post I look back at what happened, how the predictions held up, and why a weekend that also gave us a 2:01 marathon in Chicago says a lot about where endurance performance is going: faster on cool roads in new shoes, and increasingly limited by heat everywhere else.

Two races, two collapses, two champions

The men’s race. Vincent Luis won on his Kona debut in 7:47:27, taking the lead late in the marathon and holding off Gustav Iden by 1:36. At 37, and in what is expected to be the final race of his career, he became the first Frenchman to win the men’s title in Kona.

On paper the race belonged to someone else for most of the day. Magnus Ditlev came off the swim in 30th place and more than three minutes down, and still led into T2 after a 4:06:47 ride. By 30 km of the run he was fading, walking aid stations and stopping for extra ice in his cap, and Luis went past him, followed closely by Iden. Ditlev later described it as the lights going out, which is about as good a lay description of exertional heat strain as you will find.

The attrition was remarkable. Sam Laidlow walked early in the run and withdrew, explaining that he had been struggling with iliac artery endofibrosis. Patrick Lange, three-time champion and course record holder, stopped on the bike and pulled out of his final Kona, and Marten Van Riel was seen vomiting on the bike. Kristian Blummenfelt, one of the clear pre-race favourites, faded to eighth. Officiating also shaped the podium: Iden served a three-minute bike penalty, started the run close to nine minutes down and then posted the fastest run of the day, while Matthew Marquardt dropped from first to ninth after his own penalty and fought back to take third.

The women’s race was even more dramatic. Lucy Charles-Barclay led the swim in 51:29, her seventh swim lead in seven Kona starts, and swapped the lead with Taylor Knibb for much of the bike before withdrawing on the run. Knibb, who was sick late in the race, was passed by defending champion Solveig Løvseth with around 4.5 miles to go. Inside the final kilometre Løvseth could no longer run, and Knibb passed her to win in 8:36:29. It was a complete reversal of last year, when Knibb’s own late collapse opened the door for Løvseth, and it came just weeks after Knibb won a fourth 70.3 world title. Løvseth got back to her feet and walked in to finish eighth.

Behind them, two athletes showed what patience is worth in Kona. Laura Philipp came out of the water more than eight minutes behind Charles-Barclay and finished second, winning the IRONMAN Pro Series. Julie Derron took third on her Kona debut despite a three-minute drafting penalty that left her eighth off the bike, and Kat Matthews came from 14th out of the water to fourth.

The podium splits tell the story.

AthleteSwimBikeRunTotal
Men1. Vincent Luis (FRA)48:314:15:422:38:367:47:27
2. Gustav Iden (NOR)50:104:17:102:36:587:49:03
3. Matthew Marquardt (USA)48:254:11:282:43:587:49:59
Women1. Taylor Knibb (USA)52:554:40:072:57:468:36:29
2. Laura Philipp (GER)1:00:034:43:002:50:438:39:34
3. Julie Derron (SUI)53:094:49:352:58:548:46:25
Sources: Slowtwitch, TRI247, Find Your Edge. Times unofficial until IRONMAN finalises results.

What the data said, and how the predictions fared

Both winning times were well off the course records. Luis was almost 12 minutes slower than Lange’s 7:35:53 from 2024, and Knibb was eight minutes slower than Løvseth’s 8:28:27 last year. The interesting part is where the time went. The run was not uniformly slow: Iden’s run course record from 2022 is 2:36:14, and he ran 2:36:58 after serving a penalty. The fastest bike splits, by contrast, were around 4:06 to 4:11, against 4:06:22 for Lange in 2024. A choppy swim and a hard, humid day redistributed the race, and it rewarded athletes who could still regulate their effort and heat load late in the marathon. On the Long Distance Triathlons Dashboard you can see this in the WBGT-versus-split plots: the run is where the environment exacts its price, and the top-three average hides how wide the spread becomes behind the leaders.

As I wrote before the race, the Rankings & Predictions page is a form-based heuristic built from the results I scrape from PTO Stats and the race-day conditions from Open-Meteo. It cannot know who has a vascular problem, who will be penalised, or whose thermoregulation will fail with a kilometre to go. It also has a structural blind spot: athletes moving up from short course, like Luis, have a thin long-distance history, so any model that rewards consistent long-course form will underrate them. The pre-race consensus had the same blind spot. Triathlete’s preview had Blummenfelt tied with Laidlow for the best odds, and neither made the podium. A model is a benchmark, not an oracle, and Kona remains the race where the benchmark is most often wrong.

Long Distance Triathlons Dashboard split-time trends
Long Distance Triathlons Dashboard rankings and predictions
The Long Distance Triathlons Dashboard: ironmandt100analysis.netlify.app

So, the next big one is Dubai T100. How is that looking?

Meanwhile on the roads: 2:01 in Chicago, 1:59 in London

The day after Kona, Chicago produced a very different kind of performance. Amos Kipruto won in 2:01:09, the second-fastest time in Chicago history and tied for fifth on the world all-time list, and Hawi Feysa defended her title in 2:13:33, moving to fourth on the women’s all-time list. Two weeks earlier in Berlin, Tigst Assefa ran 2:11:04, a course record and the third-fastest women’s marathon ever, and was still ahead of world-record pace at 40 km before her stride broke down in the final two kilometres. Guye Adola won the men’s race in a personal best of 2:02:50.

All of this sits in the shadow of the London Marathon in April, when Sabastian Sawe ran 1:59:30, the first sub-two-hour marathon in a race that counts for records, and Assefa lowered the women-only world record to 2:15:41, with the top three women all under 2:16 in the same race for the first time.

The common denominator, again, is footwear. Sawe, Yomif Kejelcha and Assefa all raced in the Adidas Adizero Adios Pro Evo 3, a shoe of around 97 g. After Chicago it has won seven of this year’s 12 Major races, and every runner on the Berlin podium wore it. The competition is not standing still: in Chicago, Sharon Lokedi was second in an Under Armour development shoe, and both Brigid Kosgei and Milkesa Mengesha took third in Nike’s unreleased Apex.

The most interesting question is not which shoe wins (it is always the athlete winning and never the shoe!) but how these shoes work, and the geometry is changing faster than the foam. Laurens van Kouwenhove has produced a superb animated analysis of Nike’s progression from the Alphafly 3 to the Alphafly 4 and the Apex, tracing the sole profiles from side-view product photos and a Nike patent drawing.

Rocker geometry of the Nike Alphafly 3, Alphafly 4 and Apex. Analysis and graphic: Laurens van Kouwenhove

Across the three shoes, Nike appears to be moving away from the steep, early rocker:

  • The forefoot apex moves back, from 67% to 64% to 61% of shoe length.
  • The rocker radius grows, from 45% to 54% to 63% of shoe length.
  • The rolling surface extends past the toes, from −7 mm to +2 mm to +8 mm.
  • The Apex is zero drop, against 8 mm for both Alphaflys.

The biomechanical consequence is captured in his estimate of the lever arm between the ground reaction force and the ankle, which is a proxy for the load on the calf and Achilles. Early in the roll-over (foot at 10°), the newer shoes shorten the lever arm: 132 mm for the Alphafly 3, against 121 mm and 125 mm. Late in push-off (foot at 45°), the order reverses: 142 mm, against 155 mm and 158 mm. In other words, the shoe appears to “hold on to the lever” for longer, shifting plantarflexor demand later in stance rather than simply reducing it.

Van Kouwenhove is careful to say these are indicative values from an unloaded, rigid-sole model. A real sole compresses, and the foot is not a rigid lever. But the direction of the change is plausible, and it matters. Much of the debate about super shoes has focused on energy return from foam and plates. Geometry that alters ankle moments could explain why benefits vary so much between runners, and why some athletes report calf and Achilles problems when changing models. A zero-drop shoe with a longer late lever arm is not a neutral swap for someone who has trained for years in an 8 mm drop.

This is also directly relevant to triathlon. In the last 10 km at Kona, calf fatigue and form breakdown are exactly where races were lost this year. How a shoe distributes load across a 2:40 to 3:00 marathon, run after five hours on the bike, is a question nobody has studied properly yet.

I want to be careful here, because “super shoes” have become a lazy explanation for everything. There is still no precise answer to how much the curved plate and resilient foam benefit the elite marathon runner, and the gains appear to vary between individuals. Pacing, fuelling at 90 to 120 g/h, deep Ethiopian and Kenyan talent pools and cool, flat courses all contribute.

But there is a more important point for this post. These times are produced in cool conditions. Kona showed what happens to the same physiology at 30 °C and high humidity. The next marginal gains in endurance sport may come less from returning energy at the foot and more from removing heat from the body.

Cooling clothing: from F1 cockpits to tennis courts and trails

This is where the sportswear industry has been moving fast in 2026, and two companies have taken quite different approaches.

adidas CLIMACOOL+ embossed cooling fabric worn on a grass tennis court
adidas CLIMACOOL+ in use during the 2026 grass-court season. Photo: adidas.

Fabric that changes the microclimate. In June adidas launched CLIMACOOL+, built around a 3D, body-mapped embossed fabric whose raised structures hold the garment off the skin so that air can circulate and sweat can evaporate. The structures are mapped to the chest, upper abdomen, shoulders and upper back, the areas identified through physiological data capture and climate chamber testing. Interestingly for the marathon story, adidas says Sawe ran his sub-two-hour marathon in a singlet featuring the technology.

Nike Radical AirFlow racing top with funnel-shaped airducts
Nike Radical AirFlow. Photo: Nike.

Nike’s Radical AirFlow is a nice piece of applied fluid dynamics. Inspired by the Venturi effect, the material uses funnel-shaped airducts that capture air as the athlete moves forward and accelerate it towards the skin to speed up sweat evaporation. In Nike’s climate chamber testing, prototypes retained 50% less sweat than Dri-FIT after prolonged running in the heat, and thermal manikin tests showed 25% lower resistance to the evaporation of sweat. Caleb Olson wore a prototype when he won the 2025 Western States 100 in the second-fastest time in the race’s history.

Active cooling between efforts. The second adidas initiative is quite different. The CLIMACOOL System was first developed for the Mercedes-AMG PETRONAS F1 drivers to cope with cockpit heat, then deployed across adidas partner federations at the 2026 FIFA World Cup, and has now been rolled out to tennis players in New York. It consists of a vest containing a gel frozen in portable freezer units, worn over normal kit, plus neck pieces for changeovers, set breaks and practice. Adidas reports a significant cooling effect after three minutes, and an effect lasting up to an hour after 10 to 15 minutes of use.

adidas CLIMACOOL System cooling vest and neck piece used by a tennis player
The CLIMACOOL System cooling vest and neck pieces. Photo: adidas.

A few observations from a physiologist’s point of view.

First, these are two different problems. Garments like CLIMACOOL+ and Radical AirFlow try to improve heat loss during exercise by increasing air movement and evaporation at the skin. Vests and neck collars are pre-cooling and between-bout cooling tools, which suit intermittent sports like tennis and football, or the warm-up before an endurance race. The evidence that both pre-cooling and per-cooling improve performance in the heat is reasonably solid (e.g., Bongers et al., Br J Sports Med, 2015). Ditlev stuffing ice into his cap at 30 km was per-cooling in its most basic form and something you should do (I do!) in every hot race no matter how you feel at every water station [keep the body as cool as possible].

Second, the environment limits what fabric can do. Evaporation depends on the vapour pressure gradient between skin and air. In dry heat, more airflow over wet skin is very effective. In Kona’s humid air the gradient is smaller, so the gain from any garment is smaller too, although increased air velocity still helps convective heat loss. Relative air speed also matters: these garments should work better at 20 km/h on the run than at a walk, and on a bike at 40 km/h the airflow is already high.

Third, the numbers so far are manufacturer data. Fifty per cent less sweat retained and 25% lower evaporative resistance are meaningful material properties, but they are not yet independent, peer-reviewed measurements of core temperature, heat storage or performance in athletes. I would very much like to see these garments tested in proper heat-stress protocols, ideally in the kind of humid conditions that undid Ditlev and Løvseth.

What Kona 2026 teaches us

The lesson from the Big Island is the same one the literature keeps telling us: in the heat, the winner is often the athlete whose system fails last, not the one with the biggest engine. Fuelling, heat acclimatisation, pacing and cooling interact, and failures in any one of them show up in the final hour. Luis and Knibb were not the strongest athletes on paper for the whole day, but they were still running when it mattered showing that their bodies and most of all their strategies worked. If you want to read more about Kona, there is a very interesting pre-print here from 3 excellent sports scientists (and triathletes): Heat, wind, and the calendar: why a warming climate makes its performance effects unidentifiable at the IRONMAN World Championship. They have updated it now to version 3 and have also created a very nice Ironman Hawaii Predictor for age groupers based on historical data as part of the research paper. The tool is available clinking on the link below:

https://ironman-hawaii-predictor.com

Meanwhile, on cool flat roads, the marathon keeps getting faster with every new generation of shoes, and as the rocker-geometry analysis shows, not only because of foam. As temperatures at major championships rise, I expect the next wave of innovation, and the next wave of controversy, to be about thermal technology. That makes it all the more important that brands publish their data and that we as scientists test these claims independently.

Race and results information from Slowtwitch, TRI247, Triathlete, Big Island Now, NBC Sports, FloTrack, Marathon Handbook and Find Your Edge. Product information from adidas and Nike press materials. Rocker-geometry analysis by Laurens van Kouwenhove.