{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T02:31:11Z","timestamp":1773109871150,"version":"3.50.1"},"reference-count":54,"publisher":"JMIR Publications Inc.","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Mobile health (mHealth) has potential to play a significant role in realizing a reversal of the current paradigm in health care toward a more patient-centric and more collaborative system to improve the outcomes obtained along with the quality and sustainability of health care systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this study was to explore and understand individual mHealth acceptance drivers between two groups of users: those with chronic health conditions and those without.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The extended unified theory of acceptance and usage of technology (UTAUT2) was enhanced with a new health-related framework: behavior intention to recommend and new mediation effects. We applied partial least squares (PLS) causal modeling to test the research model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We obtained 322 valid responses through an online questionnaire. The drivers of behavior intention with statistical significance were performance expectancy (\u03b2=.29, P&lt;.001), habit (\u03b2=.39, P&lt;.001), and personal empowerment (\u03b2=.18, P=.01). The precursors of use behavior were habit (\u03b2= .47, P&lt;.001) and personal empowerment (\u03b2=.17, P=.01). Behavior intention to recommend was significantly influenced by behavior intention (\u03b2=.58, P&lt;.001) and personal empowerment (\u03b2=.26, P&lt;.001). The model explained 66% of the total variance in behavior intention, 54% of the variance in use behavior, and 70% of the variance in behavior intention to recommend.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study demonstrates a significant role of personal empowerment, as a second-order construct, in the mHealth acceptance context. The presence of a chronic health condition predicates an impact on acceptance of this technology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/17588","type":"journal-article","created":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T18:59:58Z","timestamp":1585594798000},"page":"e17588","source":"Crossref","is-referenced-by-count":64,"title":["Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1972-4709","authenticated-orcid":false,"given":"T\u00e2nia","family":"Salgado","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5551-8243","authenticated-orcid":false,"given":"Jorge","family":"Tavares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6523-0809","authenticated-orcid":false,"given":"Tiago","family":"Oliveira","sequence":"additional","affiliation":[]}],"member":"1010","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2196\/mhealth.7886"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5927"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2009.0156"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2012.1216"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.7389\/90596"},{"key":"ref6","unstructured":"Estat\u00edsticas Servi\u00e7o M\u00f3vel Q4 2017ANACOM20182018-09-18https:\/\/www.anacom.pt\/streaming\/STM_4T2017.pdf?contentId=1433890&field=ATTACHED_FILE"},{"key":"ref7","unstructured":"Consumer Survey on Digital Health SummaryAccenture20182018-10-28https:\/\/www.accenture.com\/us-en\/insight-new-2018-consumer-survey-digital-health"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aaa3487"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv052"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-016-0249-8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/titb.2000.5956074"},{"key":"ref12","year":"2016","journal-title":"Global diffusion of eHealth: Making universal health coverage achievable"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2143"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3564"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/17538157.2017.1363759"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3117"},{"key":"ref17","unstructured":"PfeifferJVonEMUrbachNBuchwaldAQuantify-Me: Consumer acceptance of wearable self-tracking devices20162016 European Conference on Information Systems (ECIS 2016)June 12-15, 2016Istanbul, TurkeyAssociation for Information Systems AIS Electronic Library (AISeL)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.9014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2008.03.006"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-015-0726-2"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2307\/41410412"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2196\/11032"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5069"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-017-0482-9"},{"key":"ref25","author":"Hair, J","year":"2017","journal-title":"A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 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