{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T11:44:07Z","timestamp":1789213447963,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001872","name":"Centre for the Development of Industrial Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001872","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Recovery, Transformation and Resilience Plan\u2014NextGenerationEU","award":["PAIS-20241102"],"award-info":[{"award-number":["PAIS-20241102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objective: Type 2 Diabetes Mellitus (T2DM) represents a major increasing burden for primary care systems worldwide. Digital health interventions (DHIs) have been proposed as scalable tools to improve glycemic control, yet uncertainty remains regarding which intervention characteristics yield the greatest benefit. To evaluate the effectiveness of DHIs on HbA1c levels in adults with T2DM and to examine whether intervention duration, engagement intensity, glucometer integration, and healthcare provider involvement modify glycemic outcomes. Data Sources: PubMed, Embase, Cochrane Library, and JMIR databases were systematically searched for relevant studies published between January 2020 and May 2025. Study Eligibility Criteria: Randomized controlled trials comparing DHIs plus usual care versus usual care alone in adults with T2DM and reporting HbA1c as the primary outcome. Methods: Data were extracted using the Jadad scale and TIDieR framework. Random-effects meta-analysis estimated pooled mean differences (MD) in HbA1c with 95% CIs. Subgroup analyses examined effects by intervention characteristics. Heterogeneity and sources of variance were assessed through Cochran\u2019s Q, I2, meta-regression, and sensitivity analyses (leave-one-out and trim-and-fill). Results: Thirteen RCTs (n \u2248 20,000) met inclusion criteria. DHIs achieved significant HbA1c reductions (range 0.01% to 1.57%; pooled MD \u22121.08%; 95% CI \u22121.18 to \u22120.99; p = 0.001). Short-term (\u22646 months), low-intensity interventions showed the largest effect sizes (MD \u22121.16%, 95% CI 0.94 to 1.39). Glucometer integration and healthcare provider involvement contributed minimally to additional benefit. Meta-regression confirmed substantial heterogeneity, but no single factor explained variance across studies. Limitations: Considerable heterogeneity across interventions and variability in engagement measurement may limit the generalizability of findings. Conclusions: Short-term, low-intensity DHIs significantly improve glycemic control in primary care populations with T2DM. Advanced meta-analytic techniques confirm the robustness of these effects, providing practical guidance for selecting and implementing effective digital interventions in routine diabetes care.<\/jats:p>","DOI":"10.3390\/jcm15031123","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:00:33Z","timestamp":1770022833000},"page":"1123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Characteristics of Digital Health Interventions Associated with Improved Glycemic Control in T2DM: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6032-4733","authenticated-orcid":false,"given":"Oscar Eduardo","family":"Rodr\u00edguez-Montes","sequence":"first","affiliation":[{"name":"Universidad Veracruzana, Xalapa 91000, Mexico"},{"name":"Universidad de Sevilla, 41004 Sevilla, Spain"},{"name":"Adhera Health SL, 41092 Sevilla, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2646-4079","authenticated-orcid":false,"given":"Mar\u00eda del Carmen","family":"Gogeascoechea-Trejo","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences, Universidad Veracruzana, Xalapa 91000, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-6361","authenticated-orcid":false,"given":"Clara","family":"Berm\u00fadez-Tamayo","sequence":"additional","affiliation":[{"name":"Department of Applied Economics, University of Granada, 18012 Granada, Spain"},{"name":"Ciber de Epidemiolog\u00eda y Salud P\u00fablica\u2014CIBERESP, 08950 Barcelona, Spain"},{"name":"Instituto de Investigaci\u00f3n Biosanitaria de Granada (Ibs. Granada), 18012 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"ref_1","unstructured":"International Diabetes Federation (2021). IDF Diabetes Atlas, IDF. [10th ed.]. Available online: https:\/\/fmdiabetes.org\/wp-content\/uploads\/2022\/01\/IDF_Atlas_10th_Edition_2021-comprimido.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (2018). Classification and diagnosis of diabetes: Standards of medical care in diabetes\u20142018. Diabetes Care, 41, S13\u2013S27. 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