BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHODS: To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient. RESULTS: Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase. CONCLUSIONS: The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.
BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHODS: To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient. RESULTS: Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase. CONCLUSIONS: The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.
Authors: Mattia Zanon; Giovanni Sparacino; Andrea Facchinetti; Michela Riz; Mark S Talary; Roland E Suri; Andreas Caduff; Claudio Cobelli Journal: Med Biol Eng Comput Date: 2012-06-22 Impact factor: 2.602
Authors: A A Hakim; H Petrovitch; C M Burchfiel; G W Ross; B L Rodriguez; L R White; K Yano; J D Curb; R D Abbott Journal: N Engl J Med Date: 1998-01-08 Impact factor: 91.245
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Authors: Cathal McCrory; Lisa F Berkman; Hugh Nolan; Neil O'Leary; Margaret Foley; Rose Anne Kenny Journal: Circ Res Date: 2016-06-21 Impact factor: 17.367
Authors: Sophie Huhn; Miriam Axt; Hanns-Christian Gunga; Martina Anna Maggioni; Stephen Munga; David Obor; Ali Sié; Valentin Boudo; Aditi Bunker; Rainer Sauerborn; Till Bärnighausen; Sandra Barteit Journal: JMIR Mhealth Uhealth Date: 2022-01-25 Impact factor: 4.773