JMIR Publications

ABSTRACT

Background:

The rapid increase in the number of overweight and obese people is a worldwide health problem. Obesity is often associated with physiological and mental health burdens. Due to several barriers of face-to-face psychotherapy, one promising approach is to exploit recent developments and implement innovative e-mental health interventions that offer various benefits to obese patients as well as for the healthcare system.

Objective:

This study aimed to assess the acceptance of e-mental health interventions in patients with obesity and explore its influencing predictors. In addition, the well-established, Unified Theory of Acceptance and Use of Technology model (UTAUT) will be compared with an extended UTAUT model in terms of variance explanation of acceptance.

Methods:

A cross-sectional online survey study was conducted from July 2020 to January 2021 in Germany. Eligibility requirement was adult age (18 or above), internet access, a good command of the German language, and a BMI > 30 kg/m2 (obesity). 448 patients with obesity (grade I, II and III) were recruited via specialized social media platforms. The impact of various socio-demographic, medical, and mental health characteristics were assessed. eHealth-related data and acceptance of e-mental health interventions were examined using a modified questionnaire, which is based on the UTAUT.

Results:

Acceptance of e-mental health interventions in obese patients was overall moderate (M = 3.18, SD = 1.11). There are significant differences in acceptance of e-mental health interventions among obese patients depending on the degree of obesity, age, gender, occupational status, and mental health status. In an extended UTAUT regression model acceptance was significantly predicted by the depression score (PHQ-8) (β = .07, P = .028), stress due to constant availability via mobile phone or email (β = .06, P = .024) and the confidence in using digital media (β = -.058, P = .042), as well as by the UTAUT core predictors performance expectancy (PE) (β = .45, P < .001), effort expectancy (EE) (β = .22, P < .001), and social influence (SI) (β = .27, P < .001). The comparison between an extended UTAUT model (16 predictors) and the restrictive UTAUT model (PE, EE, SI) revealed a significant difference in explained variance (F13,431= 2.366, P = .005).

Conclusions:

The UTAUT model has proven to be a valuable instrument to predict the acceptance of e-mental health interventions in patients with obesity. Furthermore, when additional predictors were added, a significantly higher percentage of explained variance in acceptance could be achieved. Based on the strong association between acceptance and future utilization, new interventions should focus on these UTAUT predictors to promote the urgently needed establishment of effective e-mental health interventions for patients with obesity, who suffer from mental health burdens.