{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T08:15:28Z","timestamp":1769156128167,"version":"3.49.0"},"reference-count":0,"publisher":"Georg Thieme Verlag KG","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p>\n            Background\u2003Patient-generated health data (PGHD) collected digitally with mobile health (mHealth) technology has garnered recent excitement for its potential to improve precision management of chronic conditions such as atrial fibrillation (AF), a common cardiac arrhythmia. However, sustained engagement is a major barrier to collection of PGHD. Little is known about barriers to sustained engagement or strategies to intervene upon engagement through application design.<\/jats:p><jats:p>\n            Objective\u2003This article investigates individual patient differences in sustained engagement among individuals with a history of AF who are self-monitoring using mHealth technology.<\/jats:p><jats:p>\n            Methods\u2003This qualitative study involved patients, health care providers, and research coordinators previously involved in a randomized, controlled trial involving electrocardiogram (ECG) self-monitoring of AF. Patients were adults with a history of AF randomized to the intervention arm of this trial who self-monitored using ECG mHealth technology for 6 months. Semistructured interviews and focus groups were conducted separately with health care providers and research coordinators, engaged patients, and unengaged patients. A validated model of sustained engagement, an adapted unified theory of acceptance and use of technology (UTAUT), guided data collection, and analysis through directed content analysis.<\/jats:p><jats:p>\n            Results\u2003We interviewed 13 patients (7 engaged, 6 unengaged), 6 providers, and 2 research coordinators. In addition to finding differences between engaged and unengaged patients within each predictor in the adapted UTAUT model (perceived ease of use, perceived usefulness, facilitating conditions), four additional factors were identified as being related to sustained engagement in this population. These are: (1) internal motivation to manage health, (2) relationship with health care provider, (3) supportive environments, and (4) feedback and guidance.<\/jats:p><jats:p>\n            Conclusion\u2003Although it required some modification, the adapted UTAUT model was useful in understanding of the parameters of sustained engagement. The findings of this study provide initial requirement specifications for the design of applications that engage patients in this unique population of adults with AF.<\/jats:p>","DOI":"10.1055\/s-0038-1672138","type":"journal-article","created":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T19:00:16Z","timestamp":1539198016000},"page":"772-781","source":"Crossref","is-referenced-by-count":33,"title":["Factors Influencing Sustained Engagement with ECG Self-Monitoring: Perspectives from Patients and Health Care Providers"],"prefix":"10.1055","volume":"09","author":[{"given":"Meghan","family":"Reading","sequence":"additional","affiliation":[{"name":"Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York, New York, United States"}]},{"given":"Dawon","family":"Baik","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, United States"}]},{"given":"Melissa","family":"Beauchemin","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, United States"}]},{"given":"Kathleen","family":"Hickey","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, United States"}]},{"given":"Jacqueline","family":"Merrill","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, New York, United States"},{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, United States"}]}],"member":"194","published-online":{"date-parts":[[2018,10,10]]},"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0038-1672138.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T17:02:22Z","timestamp":1557162142000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0038-1672138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2018,10,3]]},"published-print":{"date-parts":[[2018,10]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0038-1672138","relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10]]}}}