When Alexander Zverev walked off court after his semi-final defeat to Taylor Fritz at the Halle Open, the explanation was not tactical, physical, or psychological. It was data.
According to Reuters, Zverev said a malfunctioning medical glucose sensor left him unwell during the match, disrupting his performance at a critical moment in a three-set contest.
“I had huge problems with the sugar because the sensor I use gave me a completely incorrect reading,” he said. “It indicated very high values when they were actually low, so I injected much more insulin than I should have.” He added that during the first 45 minutes, “I had to consume about 350 grams of sugar. I felt absolutely terrible.”
Zverev, who has Type 1 Diabetes, ultimately lost 6-7(4), 6-4, 7-5, but said Taylor Fritz “deserved the win”.
And Zverev is far from the only high profile sportsperson relying on continuous glucose monitoring technology to manage his condition . CGMs have become an increasingly common sight across elite sport, with athletes including England rugby international Henry Slade, Spanish footballer Nacho Fernández, NFL tight end Noah Gray, golfer J. J. Spaun, and WNBA player Lauren Cox regularly pictured wearing the small circular sensors on their upper arms during training and competition.
Their visibility reflects how rapidly glucose monitoring has moved from a specialist medical technology to a mainstream wearable category. What began as a clinical tool for a relatively small patient population is increasingly becoming a connected health platform used by millions of people, generating a vast stream of physiological data every day.
From finger-prick tests to continuous data streams
The systems, developed by companies including Medtronic and DexCom, are based on small sensors inserted under the skin. These devices measure glucose in interstitial fluid using enzymatic electrochemical reactions, generating a continuous stream of physiological data rather than the intermittent snapshots provided by traditional finger-prick testing.
What turns these devices into IoT systems is the connected architecture surrounding them. A wearable transmitter processes and digitises the sensor signal before sending it via Bluetooth Low Energy to a smartphone, which acts as a gateway device. From there, readings can be displayed in real time, shared with caregivers or clinicians, and uploaded to Cloud platforms for longer-term analysis.
In effect, CGMs form a distributed sensing network embedded in the human body, where biological signals are continuously translated into digital data and fed into software systems designed to support decision-making.
Although Zverev clearly did not know what caused his CGM to malfunction on this particular occasion, manufacturers point out that like all biosensing systems, the devices are not infallible. According to user manuals and regulatory filings, errors can arise from physiological lag between interstitial and blood glucose, calibration drift, sensor movement, or conditions such as intense exercise, dehydration and temperature variation.
Taking centre court
CGMs have been in development for more than two decades, but their use at scale is a relatively recent phenomenon. Early systems, which emerged in the late 1990s and entered limited clinical use in the mid-2000s, were largely confined to specialist settings and required frequent calibration with traditional finger-prick blood tests.
Wider adoption has only accelerated over the past decade, as improvements in sensor accuracy, wearability and connectivity have shifted CGMs from experimental clinical tools into mainstream diabetes management systems (like the one used by Alexander Zverev) . The introduction of smartphone integration, predictive alerts and Cloud-based analytics helped turn them into connected medical IoT devices.
In June 2024, DexCom, Inc. said France had become the first European country to fully reimburse its Dexcom ONE sensor for certain people with Type 2 Diabetes using basal insulin therapy. The company said the decision reflected “a significant step forward in both the treatment and understanding of Type 2 Diabetes on both an individual and societal level.” It added: “We will continue to advocate for widening access to our life-changing technology for those living with Type 2 Diabetes.”
The move effectively turns CGMs into large-scale public health IoT infrastructure. Each sensor becomes a node in a distributed system generating continuous physiological data, extending far beyond specialist endocrinology patients.
How CGMs are evolving
At the same time, the market is shifting from vertically integrated devices towards interoperable platforms linking sensing, analytics and AI-powered automated treatment.
Medtronic is partnering with US-based Abbott Laboratories to integrate its automated insulin delivery systems with Abbott’s insulin dosing algorithms, enabling automated adjustments to therapy.
Meanwhile, start-ups such as PercuSense are developing biosensors capable of measuring multiple physiological markers simultaneously. In a 2024 first-in-human study, the company demonstrated a percutaneous device that tracked both glucose and lactate in real time during meal and exercise conditions.
The next set
Zverev’s experience at Halle illustrates how deeply connected physiological monitoring has become, not just in clinical settings but in top-level sport, where real-time biological data is increasingly embedded in decision-making.
As CGMs evolve from glucose monitors into multi-analyte platforms capable of tracking a growing range of biomarkers, their role in healthcare is likely to expand further. Millions more users are expected to gain access as healthcare providers increasingly embrace remote monitoring.
That growth, however, will bring greater scrutiny. The more patients, clinicians and healthcare systems come to rely on continuous physiological data, the less tolerance there will be for inaccurate readings, signal interruptions and other performance issues. A sensor error that might once have affected a relatively small group of specialist users could increasingly have far broader, and more serious consequences.
For manufacturers, the next challenge is therefore not simply adding new biomarkers or developing more sophisticated algorithms. It is ensuring that the devices people rely on every day are accurate, resilient and trustworthy.
Zverev described his experience as the first major sensor error he had encountered in nearly a decade of use. As glucose monitoring moves from a specialist technology to mainstream healthcare infrastructure, manufacturers will face growing pressure to ensure such incidents remain an unforced error rather than a recurring feature.
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