Cross Shane P, Alvarez-Jimenez Mario
Orygen Digital, Orygen, Parkville, Melbourne, VIC, Australia.
Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia.
Front Psychiatry. 2024 Sep 3;15:1382726. doi: 10.3389/fpsyt.2024.1382726. eCollection 2024.
Mental health disorders affect a substantial portion of the global population. Despite preferences for psychotherapy, access remains limited due to various barriers. Digital mental health interventions (DMHIs) have emerged to increase accessibility, yet engagement and treatment completion rates are concerning. Evidence across healthcare where some degree of self-management is required show that treatment engagement is negatively influenced by contextual complexity. This article examines the non-random factors influencing patient engagement in digital and face-to-face psychological therapies. It reviews established models and introduces an adapted version of the Cumulative Complexity Model (CuCoM) as a framework for understanding engagement in the context of digital mental health. Theoretical models like the Fogg Behavior Model, Persuasive System Design, Self-Determination Theory, and Supportive Accountability aim to explain disengagement. However, none adequately consider these broader contextual factors and their complex interactions with personal characteristics, intervention requirements and technology features. We expand on these models by proposing an application of CuCoM's application in mental health and digital contexts (known as DiCuCoM), focusing on the interplay between patient burden, personal capacity, and treatment demands. Standardized DMHIs often fail to consider individual variations in burden and capacity, leading to engagement variation. DiCuCoM highlights the need for balancing patient workload with capacity to improve engagement. Factors such as life demands, burden of treatment, and personal capacity are examined for their influence on treatment adherence. The article proposes a person-centered approach to treatment, informed by models like CuCoM and Minimally Disruptive Medicine, emphasizing the need for mental healthcare systems to acknowledge and address the unique burdens and capacities of individuals. Strategies for enhancing engagement include assessing personal capacity, reducing treatment burden, and utilizing technology to predict and respond to disengagement. New interventions informed by such models could lead to better engagement and ultimately better outcomes.
心理健康障碍影响着全球相当大一部分人口。尽管人们更倾向于心理治疗，但由于各种障碍，获得治疗的机会仍然有限。数字心理健康干预措施（DMHIs）应运而生，以提高可及性，然而参与度和治疗完成率却令人担忧。在需要一定程度自我管理的医疗保健领域的证据表明，治疗参与度受到情境复杂性的负面影响。本文探讨了影响患者参与数字和面对面心理治疗的非随机因素。它回顾了已有的模型，并引入了累积复杂性模型（CuCoM）的一个改编版本，作为理解数字心理健康背景下参与度的框架。像福格行为模型、说服系统设计、自我决定理论和支持性问责制等理论模型旨在解释脱离接触的原因。然而，没有一个模型充分考虑这些更广泛的情境因素及其与个人特征、干预要求和技术特征的复杂相互作用。我们通过提出CuCoM在心理健康和数字背景下的应用（称为DiCuCoM）来扩展这些模型，重点关注患者负担、个人能力和治疗需求之间的相互作用。标准化的DMHIs往往未能考虑负担和能力的个体差异，导致参与度存在差异。DiCuCoM强调需要平衡患者工作量和能力以提高参与度。研究了生活需求、治疗负担和个人能力等因素对治疗依从性的影响。本文提出了一种以患者为中心的治疗方法，以CuCoM和最小干扰医学等模型为依据，强调心理保健系统需要认识并解决个体的独特负担和能力。提高参与度的策略包括评估个人能力、减轻治疗负担以及利用技术预测和应对脱离接触。受此类模型启发的新干预措施可能会带来更好的参与度，最终带来更好的治疗效果。
