People without diabetes shouldn’t use blood sugar monitors, doctors warn
Blood sugar monitors are unnecessary for people without diabetes and could lead to eating disorders, doctors have warned.
The devices, manufactured by companies like ZOE, are part of a personalised diet trend promoted on social media which allows users to monitor nutrition levels.
The £300 programme lets participants to log their food intake and wear a glucose monitor for two weeks to measure blood sugar levels.
But NHS national diabetes advisor Professor Partha Kar said there is no strong evidence the gadgets help people without diabetes.
He warned using the technology when there’s no health reason to do so can drive an obsessive focus on numbers which, in the most extreme cases, “can translate into eating disorders”.
Meanwhile, eating disorder charity Beat told BBC News: “People with eating disorders often fixate on numbers as part of their illness, so we’d never recommend that anybody affected uses glucose monitors”.
ZOE said in a statement: “ZOE is scientifically rigorous in its approach, unrivalled by others in the industry in terms of clinical trials, robust research and a dedicated team of scientists and nutrition professionals looking to improve health through useful, evidence-based advice.”
It comes after scientists discovered a way to test whether someone is diabetic by having them speak just a few sentences into their smartphone.
A team from US-based Klick Labs created an AI model capable of distinguishing whether a person has Type 2 diabetes from six to 10 seconds of voice audio, with tests revealing an 89 per cent accuracy rate for women and 86 per cent for men.
“Our research highlights significant vocal variations between individuals with and without Type 2 diabetes and could transform how the medical community screens for diabetes,” said Jaycee Kaufman, a research scientist at Klick Labs.
“Current methods of detection can require a lot of time, travel and cost. Voice technology has the potential to remove these barriers entirely.”
The study involved analysing 18,000 recordings in order to identify acoustic features that differentiated non-diabetics from diabetics. Using signal processing, they were able to detect subtle changes in pitch and intensity that are imperceptible to the human ear.