Clinic for Psychosomatic Medicine and Psychotherapy, University of Duisburg-Essen, LVR-University Hospital Essen, 45147 Essen, Germany.
Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University of Duisburg-Essen, 45147 Essen, Germany.
Nutrients. 2022 May 8;14(9):1968. doi: 10.3390/nu14091968.
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.
超重和肥胖在生理和心理共病方面带来了巨大的负担。有各种各样的体重管理应用程序来支持减肥，但到目前为止，还缺乏对个体需要和要求的系统分析，以采取可持续的生活方式改变。本研究旨在评估超重/肥胖个体对这些应用程序的接受程度及其潜在预测因素。进行了一项横断面研究，包括 439 名超重/肥胖个体。使用基于统一接受和使用技术理论（UTAUT）的修改问卷来检查与健康相关的互联网使用和对体重管理应用程序的接受程度。体重管理应用程序的总体接受度较高，且存在显著的年龄差异。与年龄较大的个体相比，年轻个体表现出更高的接受度。BMI 与接受度无显著相关性。除心理计量数据和电子健康相关数据外，绩效预期、努力预期和社会影响也被证明是接受度的重要预测因素。扩展后的 UTAUT 模型的总方差解释率为 61.2%。了解接受度及其体重管理应用程序预测因素的相关知识可能有助于开发、优化和建立体重管理应用程序。对于确定体重管理应用程序的接受度及其预测因素，UTAUT 模型是一种有效的方法。
