Keinert Marie, Eskofier Bjoern M, Schuller Björn W, Böhme Stephanie, Berking Matthias
Department of Clinical Psychology and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany.
Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Pilot Feasibility Stud. 2023 Sep 7;9(1):155. doi: 10.1186/s40814-023-01386-z.
Stress levels and thus the risk of developing related physical and mental health conditions are rising worldwide. Dysfunctional beliefs contribute to the development of stress. Potentially, such beliefs can be modified with approach-avoidance modification trainings (AAMT). As previous research indicates that effects of AAMTs are small, there is a need for innovative ways of increasing the efficacy of these interventions. For this purpose, we aim to evaluate the feasibility of the intervention and study design and explore the efficacy of an innovative emotion-based AAMT version (eAAMT) that uses the display of emotions to move stress-inducing beliefs away from and draw stress-reducing beliefs towards oneself.
We will conduct a parallel randomized controlled pilot study at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany. Individuals with elevated stress levels will be randomized to one of eight study conditions (n = 10 per condition) - one of six variants of the eAAMT, an active control intervention (swipe-based AAMT), or an inactive control condition. Participants in the intervention groups will engage in four sessions of 20-30 min (e)AAMT training on consecutive days. Participants in the inactive control condition will complete the assessments via an online tool. Non-blinded assessments will be taken directly before and after the training and 1 week after training completion. The primary outcome will be perceived stress. Secondary outcomes will be dysfunctional beliefs, symptoms of depression, emotion regulation skills, and physiological stress measures. We will compute effect sizes and conduct mixed ANOVAs to explore differences in change in outcomes between the eAAMT and control conditions.
The study will provide valuable information to improve the intervention and study design. Moreover, if shown to be effective, the approach can be used as an automated smartphone-based intervention. Future research needs to identify target groups benefitting from this intervention utilized either as stand-alone treatment or an add-on intervention that is combined with other evidence-based treatments.
The trial has been registered in the German Clinical Trials Register (Deutsches Register Klinischer Studien; DRKS00023007 ; September 7, 2020).
全球范围内，压力水平以及由此产生的相关身心健康问题的风险正在上升。功能失调的信念会导致压力的产生。潜在地，这种信念可以通过趋避修正训练（AAMT）来改变。正如先前的研究表明，AAMT的效果较小，因此需要创新方法来提高这些干预措施的效果。为此，我们旨在评估该干预措施和研究设计的可行性，并探索一种基于情感的创新型AAMT版本（eAAMT）的效果，该版本利用情感展示将诱发压力的信念从自身移开，并将减轻压力的信念吸引到自身。
我们将在德国埃尔朗根 - 纽伦堡弗里德里希 - 亚历山大大学进行一项平行随机对照试验性研究。压力水平较高的个体将被随机分配到八个研究组之一（每组n = 10）——eAAMT的六种变体之一、一种积极对照干预（基于滑动的AAMT）或一种消极对照条件。干预组的参与者将连续四天参加四节时长为20 - 30分钟的（e）AAMT训练。消极对照条件下的参与者将通过在线工具完成评估。在训练前后以及训练完成后1周进行非盲评估。主要结局指标将是感知压力。次要结局指标将是功能失调的信念、抑郁症状、情绪调节技能和生理压力指标。我们将计算效应量并进行混合方差分析，以探索eAAMT组与对照组在结局变化方面的差异。
该研究将为改进干预措施和研究设计提供有价值的信息。此外，如果证明有效，该方法可作为基于智能手机的自动化干预措施使用。未来的研究需要确定受益于这种干预措施的目标群体，该干预措施既可以作为独立治疗方法，也可以作为与其他循证治疗方法相结合的附加干预措施。
该试验已在德国临床试验注册中心注册（德国临床试验注册；DRKS00023007；2020年9月7日）。
