Trinity Biotech says CGM+ data can flag nocturnal false-low glucose readings
The company reported that clinical wear data from a pre-pivotal trial support a feature meant to tell true low blood sugar apart from sensor-compression artifacts.
Trinity Biotech plc (Nasdaq: TRIB) said on July 2, 2026 that it had reviewed clinical study results showing its CGM+ wearable biosensor platform successfully delivered a purpose-built capability to identify nocturnal compression-related false low glucose events, addressing a common limitation of conventional continuous glucose monitoring systems.1
The data came from roughly 5,000 hours of device wear data gathered from insulin-dependent people with diabetes during a pre-pivotal clinical trial that wrapped up in the second quarter of 2026.1 Using that data, the company built an algorithm designed to distinguish false low glucose readings caused by nighttime sensor compression from actual changes in blood glucose, by combining glucose readings with other physiological signals from CGM+'s sensor architecture.1
Trinity Biotech noted that published academic research has found compression lows occur on average once every 5 to 6 days of CGM wear.1 Such events matter clinically: in automated insulin delivery systems, a mistaken low reading can cause a pump to cut back or halt insulin delivery, which may raise the risk of high blood sugar later on.1 For patients managing diabetes without a pump, a false alarm can prompt someone to eat carbohydrates for a low that never happened,1 which can then require a correction for the resulting high glucose, adding swings sometimes called "rollercoasting."1
CEO John Gillard said the device was built to go beyond glucose measurement by pairing continuous glucose monitoring with other physiological data such as heart activity, body temperature, and physical activity in one wearable.1 The company said the nocturnal compression-low detection feature is meant to be added to CGM+ as it advances toward a pivotal trial and regulatory submission.1
Written by readthrough’s AI from the linked primary sources and fact-checked against them automatically before publishing. Not investment advice.