A TIME SERIES CONTROL CHART FOR MONITORING ABNORMAL BLOOD GLUCOSE LEVELS.
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| Title: | A TIME SERIES CONTROL CHART FOR MONITORING ABNORMAL BLOOD GLUCOSE LEVELS. |
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| Authors: | Kuntapa, Naarpa1, Purintrapiban, Ussaneei1 ussaneei.pur@kmutt.ac.th |
| Source: | International Journal of Industrial Engineering. 2025, Vol. 32 Issue 6, p1478-1486. 9p. |
| Subjects: | Blood sugar monitoring, Quality control charts, Autocorrelation (Statistics), Monte Carlo method, Behavior modification, Treatment of diabetes, Time series analysis |
| Abstract: | Global diabetes statistics indicate a continuous rise in prevalence and complications, highlighting the need for more effective monitoring and management strategies. Selecting techniques for monitoring blood glucose levels is essential in detecting abnormalities, identifying root causes, and facilitating behavioral adjustments. This study proposes a control chart constructed by using a robust estimator concept, which is suitable for monitoring the autocorrelated blood glucose data as a time-series control chart based on σARMA. Its performance is evaluated by using a Monte Carlo simulation under varying parameters and compared with existing charts based on the average run length. Results will show that the proposed chart is the quickest in detecting abnormalities when the data are highly correlated and performs comparably in medium-to-low correlations. It is also applied to real patient self-monitoring data and interpreted with treatment guidelines to support behavioral adjustment. A case study will confirm its capability, particularly when used with physician guidance. The proposed chart provides timely behavior-linked insights, enhancing diabetes management. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Industrial Engineering is the property of International Journal of Industrial Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191555993 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A TIME SERIES CONTROL CHART FOR MONITORING ABNORMAL BLOOD GLUCOSE LEVELS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kuntapa%2C+Naarpa%22">Kuntapa, Naarpa</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Purintrapiban%2C+Ussaneei%22">Purintrapiban, Ussaneei</searchLink><relatesTo>1</relatesTo><i> ussaneei.pur@kmutt.ac.th</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Industrial+Engineering%22">International Journal of Industrial Engineering</searchLink>. 2025, Vol. 32 Issue 6, p1478-1486. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Blood+sugar+monitoring%22">Blood sugar monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink><br /><searchLink fieldCode="DE" term="%22Autocorrelation+%28Statistics%29%22">Autocorrelation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+modification%22">Behavior modification</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+of+diabetes%22">Treatment of diabetes</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Global diabetes statistics indicate a continuous rise in prevalence and complications, highlighting the need for more effective monitoring and management strategies. Selecting techniques for monitoring blood glucose levels is essential in detecting abnormalities, identifying root causes, and facilitating behavioral adjustments. This study proposes a control chart constructed by using a robust estimator concept, which is suitable for monitoring the autocorrelated blood glucose data as a time-series control chart based on σARMA. Its performance is evaluated by using a Monte Carlo simulation under varying parameters and compared with existing charts based on the average run length. Results will show that the proposed chart is the quickest in detecting abnormalities when the data are highly correlated and performs comparably in medium-to-low correlations. It is also applied to real patient self-monitoring data and interpreted with treatment guidelines to support behavioral adjustment. A case study will confirm its capability, particularly when used with physician guidance. The proposed chart provides timely behavior-linked insights, enhancing diabetes management. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Industrial Engineering is the property of International Journal of Industrial Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.23055/ijietap.2025.32.6.11211 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1478 Subjects: – SubjectFull: Blood sugar monitoring Type: general – SubjectFull: Quality control charts Type: general – SubjectFull: Autocorrelation (Statistics) Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Behavior modification Type: general – SubjectFull: Treatment of diabetes Type: general – SubjectFull: Time series analysis Type: general Titles: – TitleFull: A TIME SERIES CONTROL CHART FOR MONITORING ABNORMAL BLOOD GLUCOSE LEVELS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kuntapa, Naarpa – PersonEntity: Name: NameFull: Purintrapiban, Ussaneei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10724761 Numbering: – Type: volume Value: 32 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Industrial Engineering Type: main |
| ResultId | 1 |
