Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional Study.
Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional Study.
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使用基于网络的定量调查确定电子心理健康干预措施在数字精神病学中的接受度：横断面研究。
DOI：
10.2196/27436
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发表时间：
2021-07-30
影响因子：
2.2
通讯作者：
Bäuerle A
中科院分区：
文献类型：
--
作者：
Damerau M;Teufel M;Musche V;Dinse H;Schweda A;Beckord J;Steinbach J;Schmidt K;Skoda EM;Bäuerle A
糖尿病是一种非常常见的慢性疾病，给患者带来巨大的生理和心理负担。精神卫生保健的数字化产生了有效的电子精神卫生方法，为患者治疗提供了毋庸置疑的实用价值。然而，在实施和优化电子心理健康工具之前，应首先确定其接受程度和潜在障碍和资源，以制定和建立有效的以患者为导向的干预措施。本研究旨在评估糖尿病患者对电子心理健康干预的接受程度，并探讨其潜在的障碍和资源。一项横断面研究于2020年4月9日至2020年6月15日在德国进行，通过基于网络的糖尿病渠道招募患者。资格要求是成年人（18岁或以上），良好的德语水平，互联网接入和糖尿病诊断。接受度测量使用修改后的问卷，这是基于公认的统一理论的接受和使用的技术（UTAUT）和评估健康相关的互联网使用，接受电子心理健康干预措施，其障碍和资源。心理健康的测量使用验证和建立的工具，即广泛性焦虑症量表-7，患者健康问卷-2，和痛苦温度计。此外，还收集了有关糖尿病的社会人口统计学和医学数据。在开始调查的340名参与者中，有261人（76.8%）完成了调查，最终样本包括258名拥有完整数据集的参与者。糖尿病患者对电子心理健康干预的接受程度总体上是中等的（平均值3.02，SD 1.14）。性别和有精神障碍对接受有显着影响（P<.001）。在扩展的UTAUT回归模型（UTAUT预测因子加上社会人口统计学和心理健康变量）中，苦恼（β=.11; P=.03）以及UTAUT预测因子表现预期（β=.50; P<.001），努力预期（β=.15; P=.001）和社会影响（β=.28; P<.001）显著预测接受。扩展的UTAUT回归模型（13个预测因子）和仅UTAUT回归模型（表现期望、努力期望、社会影响）之间的比较显示，解释方差无显著差异（F10，244=1.567; P= 0.12）。本研究支持UTAUT模型及其预测因子在评估糖尿病患者接受电子心理健康干预方面的可行性。三个UTAUT预测因子在接受度上达到了75%的显著解释方差，表明它是一种非常有用和有效的方法来测量糖尿病患者的电子心理健康干预接受度。由于接受和使用之间的密切联系，应促进以这三个UTAUT预测因子为重点的促进接受的干预措施，以提出在精神糖尿病学中建立有效的电子精神卫生干预措施的迫切需要。
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. This study aims to assess the acceptance of e-mental health interventions among patients with diabetes and explore its underlying barriers and resources. 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. 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 (β=.11; P=.03) as well as the UTAUT predictors performance expectancy (β=.50; P<.001), effort expectancy (β=.15; P=.001), and social influence (β=.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). 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.
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