The DOI matches its Crossref record; 7 of the 9 references it cites were found in a registry or an index.
Physiologic Change in Hypoglycemia in Diabetes Patients in Rajavithi Hospital Using Non-Invasive Hypoglycemic Warning Wearable Devices: A Pilot Study
Open accessAbstract
Background: Hypoglycemic events are serious side effects which can occur when intensive glycemic control is being used to prevent vascular complications in diabetic patients. A wearable device which warns of impending hypoglycemia may help to achieve better control of diabetes. Objective: To identify physiologic changes during hypoglycemic events in diabetic patients using a wearable device. Materials and Methods: This was a pilot study of 28 glycemic events from 10 participants who used wearable hypoglycemic devices with Continuous Glucose Monitors (CGMs) in order to confirm hypoglycemic events during the study period. Variations in skin body temperature, pulse rate and skin resistance were also analysed. Data from the wearable hypoglycemic devices were collected and compared with those from CGMs to find significant variables during the hypoglycemic events. Results: Decrement of body temperature (Min BT-Mode BT) was greater in the hypoglycemic event group (-1.73±2.07 compared to -0.07±0.51in the non-hypoglycemic event group). Increment of heart rate (Max HR-Mode HR) was also higher in the hypoglycemic event group at 30.57±22.08 compared to 13.79±20.04. Decrement of skin resistance (Min SR-Mode SR) was -50.89±44.95 in hypoglycemic event group compared to -7.47±22.60 in non-hypoglycemic event group. All these physiologic changes were statistically significant with p-values= 0.015, 0.046 and 0.002 respectively. Conclusion: This is the first time a scoring system for hypoglycemic response from wearable devices has been used in Rajavithi Hospital.
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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 [31]
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 [31]
Commonly used features are supported by prior work to be relevant to the effects of DM.
“commonly used features supported by prior work to be relevant to the effects of DM31–33,35–38,45,49”
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 [31]
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 [31]
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 [31]
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 [31]
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 [31]
Citations
Cites
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The UK Prospective Diabetes Study (UKPDS): clinical and therapeutic implications for type 2 diabetes
King, P., Peacock, I., & Donnelly, R. (1999) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.1999.00092.x · Cited by 737 · cited 2×
“Results of the Diabetes Control and Complication Trial (DCCT) Research group [1] and The UK Prospective Diabetes Study (UKPDS) [2] showed, at the end of the initial trials, that intensive glycemic control significantly lowered the risk of microvascular complications (retinopathy and nephropathy) and follow-up trials […”
BackgroundAction to Control Cardiovascular Risk in Diabetes (ACCORD) Trial: Design and Methods
Buse, J. B. (2007) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2007.03.003 · Cited by 661 · cited 1×
“Physicians attempted to control their patients’ diabetes using intensive glycemic control to prevent diabetic complications; however, later diabetes landmark studies added more information about long-standing diabetes (with a duration of over 10 years) treated with more aggressive glycemic controls, and it was discove…”
BackgroundGale, E. A. M., Bennett, T. M., Macdonald, I. A., Holst, J. J., & Matthews, J. A. (1983) · Clinical Science · doi:10.1042/cs0650263 · Cited by 50 · cited 1×
“Actigraphy has so far been successfully used for analysis of pathological sleep in adults ([7,8]).”
MethodGore, M., & Mcguire, D. K. (2009) · Diabetes & vascular disease research · doi:10.3132/dvdr.2009.012 · Cited by 44 · cited 1×
“complications (retinopathy and nephropathy) and follow-up trials [3,4] showed that it also lowered the risk of macrovascular problems and mortality.”
BackgroundClinical Evaluation of a Noninvasive Alarm System for Nocturnal Hypoglycemia
Skladnev, V. N., Ghevondian, N., Tarnavskii, S., Paramalingam, N., & Jones, T. W. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400109 · Cited by 20 · cited 1×
“Unlike the “Hypomon study” ([9]), our study did not show”
ResultSchechter, A., Eyal, O., Zuckerman-Levin, N., Amihai-Ben-Yaacov, V., Weintrob, N., & Shehadeh, N. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0002 · Cited by 12
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2 of the 9 references couldn’t be matched to a known paper.
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Viswanath, V. K., Navaneethan, S., Burks, J. H., Soltani, S., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., Wang, E. J., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01501-0
“Additionally, while pre-viousworkfocusedonHRandHRV 22 – 25 ,weextracteddiurnalandultradian features of distal body temperature and sleep and assessed whether these features reveal complementary information to HR and HRV about physiology consistent with DM 31 .”
Influential
Metadata
- Authors
- Niramitmahapanya, S., & Yotha, D.
- Affiliations
- Rajavithi Hospital (TH)
- Venue
- International Journal of Innovative Research in Medical Science
- Published
- 2021-07-15
- Volume
- 6
- Issue
- 07
- Pages
- 414-417
- ISSN
- 2455-8737 (ISSN-L)
- OpenAlex
- W3185675535
- Cited by
- 1
- References
- 9
- Citations by year
- 2026: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- International Journal of Innovative Research in Medical Science (publishedVersion, cc-by-nc-sa)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- hypoglycemia, diabetes mellitus, continuous glucose monitoring, heart rate, skin resistance
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