Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis
DC Field | Value | Language |
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dc.contributor.author | Kim, Kyoung Jin | - |
dc.contributor.author | Lee, Jung-Been | - |
dc.contributor.author | Choi, Jimi | - |
dc.contributor.author | Seo, Ju Yeon | - |
dc.contributor.author | Yeom, Ji Won | - |
dc.contributor.author | Cho, Chul-Hyun | - |
dc.contributor.author | Bae, Jae Hyun | - |
dc.contributor.author | Kim, Sin Gon | - |
dc.contributor.author | Lee, Heon-Jeong | - |
dc.contributor.author | Kim, Nam Hoon | - |
dc.date.accessioned | 2022-08-09T02:40:25Z | - |
dc.date.available | 2022-08-09T02:40:25Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 2093-596X | - |
dc.identifier.issn | 2093-5978 | - |
dc.identifier.uri | https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61289 | - |
dc.description.abstract | Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation???maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 대한내분비학회 | - |
dc.title | Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.3803/EnM.2022.1479 | - |
dc.identifier.scopusid | 2-s2.0-85147113443 | - |
dc.identifier.wosid | 000830384900017 | - |
dc.identifier.bibliographicCitation | Endocrinology and Metabolism, v.37, no.3, pp 547 - 551 | - |
dc.citation.title | Endocrinology and Metabolism | - |
dc.citation.volume | 37 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 547 | - |
dc.citation.endPage | 551 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002855640 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Endocrinology & Metabolism | - |
dc.relation.journalWebOfScienceCategory | Endocrinology & Metabolism | - |
dc.subject.keywordPlus | SLEEP | - |
dc.subject.keywordAuthor | Life style | - |
dc.subject.keywordAuthor | Diabetes mellitus | - |
dc.subject.keywordAuthor | type 2 | - |
dc.subject.keywordAuthor | Glycemic control | - |
dc.subject.keywordAuthor | Fitness trackers | - |
dc.subject.keywordAuthor | Cluster analysis | - |
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