Schröder Julia, Bäuerle Alexander, Jahre Lisa Maria, Skoda Eva-Maria, Stettner Mark, Kleinschnitz Christoph, Teufel Martin, Dinse Hannah
Clinic for Psychosomatic Medicine and Psychotherapy, LVR-University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University of Duisburg-Essen, Essen, Germany.
Ther Adv Neurol Disord. 2023 May 27;16:17562864231175730. doi: 10.1177/17562864231175730. eCollection 2023.
Post-COVID-19 syndrome is a new and debilitating disease without adequate treatment options. eHealth could be a reasonable approach for symptom management.
This study aims to evaluate the acceptance for eHealth interventions for symptom management in individuals with post-COVID-19 syndrome, as well as drivers and barriers influencing acceptance.
Cross-sectional study.
This study was conducted from January 19 until 24 May 2022. Recruitment took place with a web-based survey. Acceptance and predictors of eHealth interventions were measured by the extended UTAUT model. Included in the model were the core predictor performance expectancy, social influence, and effort expectancy. Previously diagnosed mental illness was estimated and mental health by using the well-established Generalized Anxiety Disorder Scale-7 and the Patient Health Questionnaire Depression Scale. The effect of sociodemographic and medical data was assessed. Multiple hierarchical regression analyses as well as group comparisons were performed.
342 individuals with post-COVID-19 syndrome were examined. The acceptance of eHealth interventions for symptom management was moderate to high (M = 3.60, SD = 0.89). Acceptance was significantly higher in individuals with lower/other education, patients with moderate to severe symptoms during initial COVID-19 infection, still significantly impaired patients, and individuals with a mental illness. Identified predictors of acceptance were age (β = .24, < .001), current condition including moderate (β = .49, = .002) and still significantly impaired (β = .67, < .001), digital confidence (β = .19, < .001), effort expectancy (β = .26, < .001), performance expectancy (β = .33, < .001), and social influence (β = .26, < .001).
Patients with post-COVID-19 syndrome reported a satisfying level of acceptance and drivers and barriers could be identified. These factors need to be considered for the implementation and future use of eHealth interventions.
新冠后综合征是一种新型的使人衰弱的疾病，目前尚无足够的治疗方案。电子健康可能是一种合理的症状管理方法。
本研究旨在评估新冠后综合征患者对电子健康干预措施进行症状管理的接受程度，以及影响接受程度的驱动因素和障碍。
横断面研究。
本研究于2022年1月19日至5月24日进行。通过网络调查进行招募。采用扩展的UTAUT模型测量电子健康干预措施的接受程度和预测因素。该模型包括核心预测因素绩效期望、社会影响和努力期望。通过使用成熟的广泛性焦虑障碍量表-7和患者健康问卷抑郁量表来评估先前诊断的精神疾病和心理健康状况。评估社会人口统计学和医学数据的影响。进行了多重分层回归分析以及组间比较。
对342名新冠后综合征患者进行了检查。电子健康干预措施用于症状管理的接受程度为中等至高（M = 3.60，标准差 = 0.89）。在受教育程度较低/其他的个体、初次感染新冠期间有中度至重度症状的患者、仍有明显功能障碍的患者以及患有精神疾病的个体中，接受程度显著更高。确定的接受程度预测因素为年龄（β = 0.24，P < 0.001）、当前状况，包括中度（β = 0.49，P = 0.002）和仍有明显功能障碍（β = 0.67，P < 0.001）、数字信心（β = 0.19，P < 0.001）、努力期望（β = 0.26，P < 0.001）、绩效期望（β = 0.33，P < 0.001）和社会影响（β = 0.26，P < 0.001）。
新冠后综合征患者报告了令人满意的接受程度水平，并且可以识别驱动因素和障碍。在实施和未来使用电子健康干预措施时需要考虑这些因素。
