When Alexander Zverev left the court following his semi-final loss to Taylor Fritz at the Halle Open, the reason had nothing to do with strategy, fitness, or nerves. It came down to faulty data.
As Reuters reports, Zverev explained that a malfunctioning glucose sensor used for medical purposes made him feel unwell during the match, hampering his performance at a crucial juncture in a three-set battle.
“I had serious issues with my blood sugar because the sensor I wear gave me entirely wrong readings,” he said. “It showed very high levels when they were actually low, so I administered far more insulin than necessary.” He also noted that during the first 45 minutes, “I had to ingest around 350 grams of sugar. I felt completely awful.”
Zverev, who lives with Type 1 Diabetes, eventually fell 6-7(4), 6-4, 7-5, but acknowledged that Taylor Fritz “deserved to win.”
And Zverev is hardly the only prominent athlete turning to continuous glucose monitoring technology to help manage their condition. CGMs have become an increasingly familiar sight across elite sport. Athletes such as England rugby international Henry Slade, Spanish footballer Nacho Fernández, NFL tight end Noah Gray, golfer J. J. Spaun, and WNBA player Lauren Cox are frequently photographed with the small circular sensors affixed to their upper arms during both training and competition.
Their widespread presence signals how quickly glucose monitoring has transitioned from a niche medical device into a mainstream wearable category. What originated as a clinical instrument for a relatively limited patient base is progressively transforming into a connected health platform embraced by millions, producing an enormous flow of physiological data on a daily basis.
From occasional finger-pricks to uninterrupted data streams
The systems, created by companies such as Medtronic and DexCom, rely on tiny sensors placed beneath the skin. These devices gauge glucose levels in interstitial fluid through enzymatic electrochemical reactions, delivering a steady stream of physiological data rather than the sporadic readings associated with traditional finger-prick testing.
What elevates these gadgets into the Internet of Things is the connected framework built around them. A wearable transmitter interprets and converts the sensor signal before relaying it through Bluetooth Low Energy to a smartphone, which serves as a gateway. From there, readings can be viewed in real time, shared with caregivers or doctors, and uploaded to Cloud platforms for extended analysis.
In essence, CGMs create a distributed sensing network integrated into the human body, where biological signals are perpetually converted into digital information and channeled into software systems intended to guide decisions.
While Zverev clearly wasn’t aware of what specific factor caused his CGM to fail on that particular occasion, manufacturers stress that like any biosensing system, these devices are not without flaws. According to user guides and regulatory documents, inaccuracies can stem from the physiological delay between interstitial and blood glucose levels, calibration shifts, sensor displacement, or conditions like strenuous physical activity, dehydration, and temperature fluctuations.
Stepping into the spotlight
CGMs have been in development for over two decades, but widespread adoption is a comparatively recent development. Early versions, which surfaced in the late 1990s and saw limited clinical deployment in the mid-2000s, were mostly restricted to specialist environments and demanded regular calibration via traditional finger-prick blood tests.
Broader uptake has only picked up momentum over the past decade, as gains in sensor precision, comfort, and connectivity have propelled CGMs from experimental clinical instruments into standard diabetes management tools (such as the one relied upon by Alexander Zverev). The arrival of smartphone pairing, predictive notifications, and Cloud-driven analytics helped reshape them into connected medical IoT devices.
In June 2024, DexCom, Inc. announced that France had become the first European country to provide full reimbursement for its Dexcom ONE sensor among certain individuals with Type 2 Diabetes who are on basal insulin therapy. The company described the decision as “a significant step forward in both the treatment and understanding of Type 2 Diabetes at both an individual and societal level.” It further stated: “We will continue to push for broader access to our life-changing technology for those managing Type 2 Diabetes.”
The development effectively positions CGMs as large-scale public health IoT infrastructure. Each sensor functions as a node within a distributed architecture generating continuous physiological data, reaching well beyond specialist endocrinology patients.
How CGMs are advancing
At the same time, the industry is pivoting from vertically integrated proprietary devices toward open, interoperable platforms that connect sensing, analytics, and AI-driven automated treatment.
Medtronic is collaborating with US-based Abbott Laboratories to merge its automated insulin delivery systems with Abbott’s insulin dosing algorithms, enabling therapy to be adjusted automatically.
Meanwhile, emerging companies like PercuSense are engineering biosensors capable of tracking multiple physiological indicators at once. In a 2024 first-in-human trial, the company showcased a percutaneous device that simultaneously monitored both glucose and lactate in real time during eating and exercise scenarios.
The next phase
Zverev’s ordeal at Halle underscores just how deeply intertwined physiological monitoring has become—not merely in hospital or clinic settings but in elite sport, where real-time biological data is increasingly woven into critical decisions.
As CGMs progress from single-purpose glucose monitors into multi-analyte platforms capable of following a broader spectrum of biomarkers, their significance in healthcare is poised to grow even further. Millions additional users are expected to gain access as healthcare providers progressively adopt remote monitoring solutions.
That expansion, however, will invite closer examination. The more that patients, doctors, and healthcare systems depend on uninterrupted physiological data, the lower the tolerance will be for erroneous readings, signal dropouts, and other performance shortcomings. A sensor glitch that may once have impacted a small cluster of specialist users could increasingly carry far wider, more severe implications.
For manufacturers, the next hurdle is therefore not simply adding novel biomarkers or crafting more advanced algorithms. It is guaranteeing that the devices people rely on daily are precise, durable, and dependable.
Zverev characterised his ordeal as the first significant sensor malfunction he had experienced in almost a decade of continuous use. As glucose monitoring transitions from a specialised instrument to mainstream healthcare infrastructure, manufacturers will confront mounting pressure to ensure such episodes remain rare anomalies rather than habitual occurrences.
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