{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T22:01:12Z","timestamp":1767045672584},"reference-count":0,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2018,4]]},"abstract":"<jats:p>\n            Background\u2003Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time.<\/jats:p><jats:p>\n            Objective\u2003The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app.<\/jats:p><jats:p>\n            Methods\u2003Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs.<\/jats:p><jats:p>\n            Results\u2003Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively.<\/jats:p><jats:p>\n            Conclusion\u2003The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.<\/jats:p>","DOI":"10.1055\/s-0038-1660438","type":"journal-article","created":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T23:09:35Z","timestamp":1529536175000},"page":"440-449","source":"Crossref","is-referenced-by-count":9,"title":["Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors"],"prefix":"10.1055","volume":"09","author":[{"given":"Danielle","family":"Groat","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States"},{"name":"Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiral","family":"Soni","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Grando","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bithika","family":"Thompson","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Kaufman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Curtiss","family":"Cook","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States"},{"name":"Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2018,6,20]]},"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0038-1660438.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T20:21:17Z","timestamp":1557174077000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0038-1660438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2018,4,4]]},"published-print":{"date-parts":[[2018,4]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0038-1660438","relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4]]}}}