{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T02:50:52Z","timestamp":1789527052514,"version":"build-2803163510"},"reference-count":82,"publisher":"JMIR Publications Inc.","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n Diabetes is a very common chronic disease that exerts massive physiological and psychological burdens on patients. The digitalization of mental health care has generated effective e-mental health approaches, which offer an indubitable practical value for patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be first determined for developing and establishing effective patient-oriented interventions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to assess the acceptance of e-mental health interventions among patients with diabetes and explore its underlying barriers and resources.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A cross-sectional study was conducted in Germany from April 9, 2020, to June 15, 2020, through a web-based survey for which patients were recruited via web-based diabetes channels. The eligibility requirements were adult age (18 years or older), a good command of the German language, internet access, and a diagnosis of diabetes. Acceptance was measured using a modified questionnaire, which was based on the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) and assessed health-related internet use, acceptance of e-mental health interventions, and its barriers and resources. Mental health was measured using validated and established instruments, namely the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-2, and Distress Thermometer. In addition, sociodemographic and medical data regarding diabetes were collected.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Of the 340 participants who started the survey, 261 (76.8%) completed it and the final sample comprised 258 participants with complete data sets. The acceptance of e-mental health interventions in patients with diabetes was overall moderate (mean 3.02, SD 1.14). Gender and having a mental disorder had a significant influence on acceptance (P<.001). In an extended UTAUT regression model (UTAUT predictors plus sociodemographics and mental health variables), distress (\u03b2=.11; P=.03) as well as the UTAUT predictors performance expectancy (\u03b2=.50; P<.001), effort expectancy (\u03b2=.15; P=.001), and social influence (\u03b2=.28; P<.001) significantly predicted acceptance. The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT-only regression model (performance expectancy, effort expectancy, social influence) revealed no significant difference in explained variance (F10,244=1.567; P=.12).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study supports the viability of the UTAUT model and its predictors in assessing the acceptance of e-mental health interventions among patients with diabetes. Three UTAUT predictors reached a notable amount of explained variance of 75% in the acceptance, indicating that it is a very useful and efficient method for measuring e-mental health intervention acceptance in patients with diabetes. Owing to the close link between acceptance and use, acceptance-facilitating interventions focusing on these three UTAUT predictors should be fostered to bring forward the highly needed establishment of effective e-mental health interventions in psychodiabetology.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/27436","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T22:45:15Z","timestamp":1622501115000},"page":"e27436","source":"Crossref","is-referenced-by-count":45,"title":["Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional 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