Background The rapid increase in the number of people who are overweight and obese is a worldwide health problem. Obesity is often associated with physiological and mental health burdens. Owing to several barriers to face-to-face psychotherapy, a promising approach is to exploit recent developments and implement innovative e–mental health interventions that offer various benefits to patients with obesity and to the health care system. Objective This study aims 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 (UTAUT) model is compared with an extended UTAUT model in terms of variance explanation of acceptance. Methods A cross-sectional web-based survey study was conducted from July 2020 to January 2021 in Germany. Eligibility requirements were adult age (≥18 years), internet access, good command of the German language, and BMI >30 kg/m2 (obesity). A total of 448 patients with obesity (grades I, II, and III) were recruited via specialized social media platforms. The impact of various sociodemographic, medical, and mental health characteristics was assessed. eHealth-related data and acceptance of e–mental health interventions were examined using a modified questionnaire based on the UTAUT. Results Overall, the acceptance of e–mental health interventions in patients with obesity was moderate (mean 3.18, SD 1.11). Significant differences in the acceptance of e–mental health interventions among patients with obesity exist, depending on the grade of obesity, age, sex, occupational status, and mental health status. In an extended UTAUT regression model, acceptance was significantly predicted by the depression score (Patient Health Questionnaire-8; β=.07; P=.03), stress owing to constant availability via mobile phone or email (β=.06; P=.02), and confidence in using digital media (β=−0.058; P=.04) and by the UTAUT core predictors performance expectancy (β=.45; P<.001), effort expectancy (β=.22; P<.001), and social influence (β=.27; P<.001). The comparison between an extended UTAUT model (16 predictors) and the restrictive UTAUT model (performance expectancy, effort expectancy, and social influence) 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. The extended UTAUT model explained a significantly high percentage of variance in acceptance (in total 73.6%). On the basis of the strong association between acceptance and future use, new interventions should focus on these UTAUT predictors to promote the establishment of effective e–mental health interventions for patients with obesity who experience mental health burdens.

Overweight and obesity carry a tremendous burden in terms of physiological and psychological comorbidities. There is a great variety of weight management applications to support weight reduction, but a systematical analysis of individuals’ needs and requirements to adopt sustaining lifestyle changes is missing so far. This study aimed to assess the acceptance of such applications and its underlying predictors in individuals with overweight/obesity. A cross-sectional study was conducted, including 439 overweight/obese individuals. Health-related internet use and acceptance of weight management applications were examined using a modified questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The general acceptance of weight management applications was high, with significant age differences. Compared to older individuals, younger ones showed a higher acceptance. BMI was not significantly associated with acceptance. Besides psychometric data and eHealth-related data, performance expectancy, effort expectancy, and social influence proved to be significant predictors for acceptance. The total variance explanation provided by the extended UTAUT model was 61.2%. The knowledge of the influencing factors on acceptance might be useful in developing, optimizing, and establishing weight management applications. For determining acceptance and its predictors of weight management applications, the UTAUT model is a valid approach.

Introduction: Several studies indicate an association between mental disorders and overweight or obesity. E-mental-health interventions offer an effective way to overcome barriers to health care access for individuals with overweight and obesity. The objective of this study was to examine the needs and demands for e-mental health interventions among individuals with overweight and obesity. Methods: A cross-sectional study was conducted from 2020 to 2021 in Germany. A total of 643 participants were recruited through specialized social media platforms and the Alfried-Krupp hospital in Essen, Germany. Sociodemographic and medical data were analyzed, as well as data on depressive symptoms and on the needs and demands for e-mental health interventions. Results: Contact with and recommendation by experts appear to be key aspects in the acceptance and use of e-mental health interventions. In summary, most participants preferred a 20- to 30-minute weekly session via smartphone over a four-month period. The highest preference in terms of features included practicing coping skills and being provided with information; in regard to desired topics, nutrition consultation, quality of life and adapting to new life situations were considered most important. Discussion: E-mental health interventions can be highly beneficial for individuals, especially when developed through a user-centered design approach. The results of the study indicate which content and design are preferred and, thereby, provide valuable information for consideration when developing a tailored e-mental health intervention.

Purpose: The present study investigates the impact of obesity surgery on mental health (i.e., eating behavior and distress) during the COVID-19 pandemic. Methods: Two hundred fifty-four participants were recruited via social media. One hundred fourteen (44.53%) of them were surgery candidates (waiting for obesity surgery), while 142 (55.46%) had already undergone surgery. Participants who underwent surgery were compared to participants that did not yet undergo surgery in terms of mental burden (depression and anxiety), as well as safety and eating behavior. Further moderation analyses attempted to identify risk factors for increased COVID-19-related dysfunctional eating behavior after surgery. Results: Participants who underwent surgery showed generally lower levels of depression and general anxiety on a trend level. Moderation analyses suggested that people with high levels of generalized anxiety actually show more dysfunctional COVID-19-specific eating behavior after obesity surgery. Conclusion: On a trend level, obesity surgery appears to attenuate symptoms of generalized anxiety and depression. Yet, surgery patients with high levels of generalized anxiety exhibit even higher levels of dysfunctional eating during the COVID-19 pandemic. It is therefore particularly important to support people at risk.

(1) Dietary behavior is highly relevant for patients after bariatric surgery. No instrument exists assessing adherence to medical guidelines concerning the dietary behavior of patients after bariatric surgery. The aim of this study was to develop and validate such an instrument. (2) Data from patients after bariatric surgery (n = 543) were collected from March to May 2022. The development of the DBI-S was theory-based and interdisciplinary. Items’ and content validity of the DBI-S were examined. (3) The final version of the DBI-S consists of 13 items. Convergent validation was confirmed by significant correlations between DBI-S score and attitude towards healthy food (r = 0.26, p = <0.001) and impulsivity (r = −0.26, p = <0.001). Criterion validity was confirmed by significant correlations between DBI-S score and pre-/post-surgery BMI difference (r = −0.14, p = 0.002), pre-/post-surgery weight difference (r = 0.13, p = 0.003), and quality of life (r = 0.19, p = <0.001). Cluster analysis confirmed the ability to distinguish between two dietary behavior clusters (rather healthy and rather unhealthy). (4) The DBI-S is an economic and valid instrument to assess the adherence of post-bariatric surgery patients to the relevant dietary behavior recommendations and guidelines and can distinguish between rather unhealthy and healthy dietary behavior.