Open-Source Technology for Real-Time Continuous Glucose Monitoring in the Neonatal Intensive Care Unit: Case Study in a Neonate With Transient Congenital Hyperinsulinism

Braune, Katarina; Wäldchen, Mandy; Raile, Klemens; Hahn, Sigrid A.; Ubben, Tebbe; Römer, Susanne; Hoeber, Daniela; Reibel, Nora Johanna; Launspach, Michael; Blankenstein, Oliver; Bührer, Christoph

doi:10.2196/21770

Cited by 10 publications

(7 citation statements)

References 98 publications

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“…However, as CGM is a passive form of monitoring, it can record data at a high granularity with very minimal response burden on users [ 18 ]; therefore, it has the potential to collect detailed glycemic data while being acceptable to patients on a long-term basis. There have been a limited number of studies describing the utility of CGM in patients with HI [ 19 - 22 ], and none have described the timing of hypoglycemia events or glycemic trends.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Worth¹,

Harper²,

Salomon‐Estebanez³

et al. 2021

J Med Internet Res

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Background Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal. Objective This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events. Methods Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels <3.5 mmol/L. Results A total of 449 hypoglycemic events totaling 15,610 minutes were captured over 237 days from 47 patients (29 males; mean age 70 months, SD 53). The mean length of hypoglycemic events was 35 minutes. There was a clear tendency for hypoglycemia in the early hours (3-7 AM), particularly for patients with HI older than 10 months who experienced hypoglycemia 7.6% (1480/19,370 minutes) of time in this period compared with 2.6% (2405/92,840 minutes) of time outside this period (P<.001). This tendency was less pronounced in patients with HI who were younger than 10 months, patients with a negative genetic test result, and patients with IKH. Despite real-time CGM, there were 42 hypoglycemic events from 13 separate patients with HI lasting >30 minutes. Conclusions This is the first study to have taken the first step in extending the digital phenotype of HI by describing the glycemic trends and identifying the timing of hypoglycemia measured by CGM. We have identified the early hours as a time of high hypoglycemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycemia and must target personalized hypoglycemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modeling to produce small improvements in hypoglycemia prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Worth¹,

Harper²,

Salomon‐Estebanez³

et al. 2021

J Med Internet Res

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“…Recent research demonstrates the increasingly cost-effectiveness of using CGM for patients with diabetes, with other research expanding the health context beyond diabetes patients. 24 , 42 , 43 As using data from CGM represents an exciting new way to provide personalized feedback to assist with behavioral changes, it would be important to understand how cancer survivors might perceive the usage of such wearable devices, especially given the generally older age of cancer survivors when compared to healthy populations. For example, one systematic review of literature found that age of the patient is one of the top barriers for patients in adopting telemedicine.…”

Section: Discussionmentioning

confidence: 99%

A qualitative study to explore the acceptability and usefulness of personalized biofeedback to motivate physical activity in cancer survivors

et al. 2022

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Objective Many cancer survivors do not meet recommended levels of exercise, despite the benefits physical activity offers. This study aimed to understand experiences of insufficiently active overweight/obese breast or colorectal cancer survivors, in efforts to (1) examine regular physical activity barriers, and (2) determine perceptions and acceptability of a remotely delivered physical activity intervention utilizing wearable sensors and personalized feedback messages. Methods In-person and virtual small group interviews were conducted engaging overweight/obese cancer survivors ( n = 16, 94% female, 94% breast cancer survivors) in discussions resulting in 314 pages of transcribed data analyzed by multiple coders. Results All participants expressed needing to increase physical activity, identifying lack of motivation centering on survivorship experiences and symptom management as the most salient barrier. They indicated familiarity with activity trackers (i.e., Fitbit) and expressed interest in biosensors (i.e., continuous glucose monitors [CGMs]) as CGMs show biological metrics in real-time. Participants reported (1) personalized feedback messages can improve motivation and accountability; (2) CGM acceptability is high given survivors’ medical history; and (3) glucose data is a relevant health indicator and they appreciated integrated messages (between Fitbit and CGM) in demonstrating how behaviors immediately affect one's body. Conclusions This study supports the use of wearable biosensors and m-health interventions to promote physical activity in cancer survivors. Glucose-based biofeedback provides relevant and motivating information for cancer survivors regarding their daily activity levels by demonstrating the immediate effects of physical activity. Integrating biofeedback into physical activity interventions could be an effective behavioral change strategy to promote a healthy lifestyle in cancer survivors.

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“…Kontinuierliche Glucosemesssysteme (CGMS) sind eigentlich nicht für das Säuglingsalter zugelassen. Die Messge- nauigkeit der CGMS-Systeme ist aber ähnlich wie bei größeren Kindern und wurden daher auch schon erfolgreich bei Säuglingen mit KHI verwendet [12]. Darunter kann die Messfrequenz der kapillären Blutzuckermessungen (teilweise stündlich notwendig) reduziert werden.…”

Section: Klinikunclassified

Kongenitaler Hyperinsulinismus

Rami‐Merhar

2021

J. Klin. Endokrinol. Stoffw.

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ZusammenfassungDer kongenitale Hyperinsulinismus (KHI) ist die häufigste Ursache persistierender Hypoglykämien im Säuglings- und Kindesalter; darunter präsentiert sich eine heterogene Gruppe von klinisch, genetisch und histologisch unterschiedlichen Erkrankungen. Charakterisiert ist der KHI durch eine überschießende Ausschüttung von Insulin und das Auftreten von Hypoglykämien, die unbehandelt zu schweren irreversiblen neurokognitiven Defiziten führen, daher ist eine rasche Diagnose und Behandlung essenziell. In den letzten Jahren gab es viele neue Erkenntnisse im Bereich der Genetik, Diagnostik und Therapie.

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Open-Source Technology for Real-Time Continuous Glucose Monitoring in the Neonatal Intensive Care Unit: Case Study in a Neonate With Transient Congenital Hyperinsulinism

Cited by 10 publications

References 98 publications

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

A qualitative study to explore the acceptability and usefulness of personalized biofeedback to motivate physical activity in cancer survivors

Kongenitaler Hyperinsulinismus

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