A Prototype of a New Noninvasive Device to Detect Nocturnal Hypoglycemia in Adolescents with Type 1 Diabetes—A Pilot Study
Abstract
Background: Severe hypoglycemic events are a major consequence of tight diabetes control. Continuous glucose monitoring systems (CGMSs) were recently introduced in order to minimize the risk of hypoglycemia. However, the present CGMSs are invasive and costly and have been recently demonstrated to be intolerant for most children and adolescents. Hence there is a need for a simple, noninvasive, convenient, and inexpensive device to detect hypoglycemic events. The Gili Medical Hypoglycemia Non Invasive Monitoring System (GMHNIMS) (Gili Medical Ltd., Migdal HaEmek, Israel) has been currently developed for these purposes. Subjects and Methods: Ten patients 14–18 years old with type 1 diabetes for at least 1 year participated in a pilot study that was held at the Meyer Children's Hospital, Rambam Medical Center, Haifa, Israel. All patients were either treated by insulin pump or by multiple daily injections. The GMHNIMS was connected to the study subjects during three consecutive nights in an inpatient setting while they received their usual insulin regimen. The system is composed of four sensors (heart rate, perspiration, skin temperature, and tremor) that detect physiologic changes during hypoglycemia. In addition, each patient was connected to a real-time CGMS for 3 nights. When a hypoglycemic event was suspected clinically by the patient, a bedside capillary glucose was checked by a glucometer. Results: The system was found to be convenient without any disturbances to sleep quality. The sensitivity of the GMHNIMS for detection of true hypoglycemic events was 100% with specificity of 85.7%. Conclusions: The new device showed high detection rates of nocturnal hypoglycemic events with an acceptable degree of false-positive readings. Being inexpensive and noninvasive, this device has the potential for routine use in insulin-treated patients.
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Skin temperature collected from wrist-worn wearables has been assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals.
“Skin temperature was additionally collected from wrist-worn wearables was assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Changes in statistical measures of distal body temperature can be used for identifying hypoglycemic events and predicting blood glucose concentration.
“These works analyzed how changes in statistical measures of distal body temperature can be used for identifying hypoglycemic events and prediction blood glucose concentration29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Prior work was generated from small cohorts (the largest cohort of 70 individuals), raising questions of the generalizability of their findings.
“Second, prior work was generated from small cohorts (the largest cohort of 70 individuals), that raise questions of the generalizability of their findings5,22–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Prior work typically uses less than a day of input.
“First, where prior work typically uses less than a day of input5,22–32, we assess the impact of longitudinal input windows ranging up to multiple weeks and demonstrate improved sensitivity with longer windows (AUROC increases by 0.15 from 1-night to 21-night models, in balanced and imbalanced cohorts).”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Our data is more representative of real-world performance than prior work because we captured a broader population segment with larger cohorts.
“While prior work reported higher performance, our data is more representative of real-world performance as we captured a broader population segment with our larger cohorts (balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data)5,29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features), which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.
“Third, prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features)5,26–32, which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
The balanced cohort is 5 times and the imbalanced cohort is 91 times the largest prior cohort of wearable data.
“balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
Neither prior literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.
“Though interesting, neither literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [30]
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- Authors
- Schechter, A., Eyal, O., Zuckerman-Levin, N., Amihai-Ben-Yaacov, V., Weintrob, N., & Shehadeh, N.
- Affiliations
- Gili Medical Ltd.
- Tel Aviv University (IL)
- Rambam Medical Center
- Technion – Israel Institute of Technology (IL)
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2012-08
- Volume
- 14
- Issue
- 8
- Pages
- 683-689
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2055550325
- Cited by
- 12
- References
- 20
- Citations by year
- 2026: 2 · 2025: 1 · 2022: 2 · 2020: 1 · 2019: 1 · 2018: 3 · 2015: 1 · 2013: 1
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- nocturnal hypoglycemia, type 1 diabetes, adolescents, heart rate, sweating, skin temperature, tremor, continuous glucose monitoring, insulin pump therapy, multiple daily insulin injections, sleep quality, sensitivity and specificity
- MeSH terms
- Adolescent, Algorithms, Blood Glucose, Circadian Rhythm, Cost-Benefit Analysis, Diabetes Mellitus, Type 1, Equipment Design, Female, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Israel, Male, Monitoring, Physiologic, Patient Compliance, Pilot Projects, Sensitivity and Specificity, Blood Glucose Self-Monitoring, Biosensing Techniques
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