{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:03:28Z","timestamp":1763535808322},"reference-count":37,"publisher":"Georg Thieme Verlag KG","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.<\/jats:p><jats:p>\n          Objectives\u2003This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.<\/jats:p><jats:p>\n          Methods\u2003We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols.<\/jats:p><jats:p>\n          Results\u2003The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.<\/jats:p><jats:p>\n          Conclusion\u2003We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional\/routine practice.<\/jats:p>","DOI":"10.1055\/s-0040-1719043","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T23:36:35Z","timestamp":1609976195000},"page":"001-009","source":"Crossref","is-referenced-by-count":19,"title":["Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications"],"prefix":"10.1055","volume":"12","author":[{"given":"Mirza Mansoor","family":"Baig","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"}]},{"given":"Hamid","family":"GholamHosseini","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"}]},{"given":"Jairo","family":"Gutierrez","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"}]},{"given":"Ehsan","family":"Ullah","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"}]},{"given":"Maria","family":"Lind\u00e9n","sequence":"additional","affiliation":[{"name":"School of Innovation Design and Engineering, M\u00e4lardalen University, V\u00e4ster\u00e5s, Sweden"}]}],"member":"194","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"ref1","first-page":"91","article-title":"Obesity risk assessment model using wearable technology with personalized activity, calorie expenditure and health profile","volume":"261","author":"H Gholamhosseini","year":"2019","journal-title":"Stud Health Technol Inform"},{"issue":"08","key":"ref3","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10916-019-1365-7","article-title":"A systematic review of wearable sensors and IoT-based monitoring applications for older adults - a focus on ageing population and independent living","volume":"43","author":"M M Baig","year":"2019","journal-title":"J Med Syst"},{"key":"ref4","first-page":"1","volume-title":"Managing long-term conditions: wearable sensors and IoT-based monitoring applications","author":"M M Baig","year":"2019"},{"issue":"04","key":"ref5","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1055\/s-0038-1676458","article-title":"Comparing real-time self-tracking and device-recorded exercise data in subjects with type 1 diabetes","volume":"9","author":"D Groat","year":"2018","journal-title":"Appl Clin Inform"},{"issue":"02","key":"ref6","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1055\/s-0038-1660438","article-title":"Design and testing of a smartphone application for real-time self-tracking diabetes self-management behaviors","volume":"9","author":"D Groat","year":"2018","journal-title":"Appl Clin Inform"},{"issue":"03","key":"ref7","first-page":"854","article-title":"An embedded mobile ECG reasoning system for elderly patients. 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