JMIR Publications

ABSTRACT

Background:

Diabetes is a very common chronic disease, which confronts patients with massive physiological and psychological burdens. The digitalization of mental health care has generated effective e-mental health approaches, which bear indubitable practical value to patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be determined first in order to be able to develop and establish effective patient-oriented interventions.

Objective:

This study aimed to assess the acceptance of e-mental health interventions in diabetes patients and to explore its underlying barriers and resources.

Methods:

A cross-sectional study was conducted in Germany over a period of two months in 2020 through an online survey recruited via online diabetes channels. Eligibility requirement was adult age (18 or above), 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, the Patient Health Questionnaire-2 and the Distress Thermometer. Additionally, socio-demographic and medical data regarding diabetes were asked

Results:

Of 340 participants starting the survey 76.8 % completed it, resulting in 261 participants and a final sample of 258 participants with complete datasets. The acceptance of e-mental health interventions in diabetes patients was overall moderate (M = 3.02, SD = 1.14). Sex and suffering from a mental disorder had a significant influence on acceptance (P < .001). In an extended UTAUT regression model (UTAUT predictors plus socio-demographics and mental health variables) acceptance was significantly predicted by distress (β = .11, P = .027) as well as by the UTAUT predictors performance expectancy (PE) (β = .50, P < .001), effort expectancy (EE) (β = .15, P = .001), and social influence (SI) (β = .28, P < .001). The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT only regression model (PE, EE, SI) revealed no significant difference in explained variance (F10,244 = 1.567, P =.117).

Conclusions:

This study supports the viability of the UTAUT model and its predictors in assessing acceptance of e-mental health interventions in diabetes patients. Three UTAUT predictors reached a notable amount of explained variance in acceptance of 75 %, indicating being a very useful and efficient method for measuring e-mental health intervention acceptance of diabetic patients. Due to the close link between acceptance and utilization, 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.