{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:17:19Z","timestamp":1777630639011,"version":"3.51.4"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2D), using an analytic framework generalizable to other chronic conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A systematic review and meta-analysis of randomized controlled trials were conducted to estimate the synthesized effect of m-health interventions on MA. These results were integrated into a Markov state-transition model to simulate patient transitions among three adherence levels over a 10-year horizon. Medical expenditure data by adherence level were derived from the Medical Expenditure Panel Survey (MEPS). Monte Carlo simulation was applied to assess uncertainty and estimate individual- and population-level cost outcomes under baseline and intervention scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The meta-analysis showed a significant positive effect of m-health on MA (standardized mean difference\u2009=\u20090.21, 95% CI: 0.14\u20130.28). Patients in the intervention scenario experienced an average cost reduction of $4400 over 10 years. At the population level, a cohort of 10\u2009000 patients using m-health tools would yield projected direct medical cost savings of $44 million.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study demonstrates the potential of m-health interventions to improve patient behavior and generate substantial long-term cost savings. By linking behavioral health data to downstream cost outcomes, the study adds to the growing evidence base for informatics-driven population health strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study underscores the importance of integrating digital support tools into chronic disease care and informs policy decisions aimed at integrating health informatics innovations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf199","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T13:10:37Z","timestamp":1762434637000},"page":"347-358","source":"Crossref","is-referenced-by-count":1,"title":["Does it save me money? The economic impact of mobile health interventions on medical expenditure of diabetic patients"],"prefix":"10.1093","volume":"33","author":[{"given":"Xinying","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computing Sciences, University of Houston-Clear Lake , Houston, TX 77058,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8174-3903","authenticated-orcid":false,"given":"Upkar","family":"Varshney","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Georgia State University , Atlanta, GA 30302,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Analytics, Northern Kentucky University , Highland Heights, KY 41099,","place":["United 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