Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK
Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK.
BMJ Open. 2020 Mar 8;10(3):e032312. doi: 10.1136/bmjopen-2019-032312.
We aimed to develop a digital intervention to support antidepressant discontinuation in UK primary care that is scalable, accessible, safe and feasible. In this paper, we describe the development using a theory, evidence and person-based approach.
Intervention development using a theory, evidence and person-based approach.
Primary Care in the South of England.
Fifteen participants with a range of antidepressant experience took part in 'think aloud' interviews for intervention optimisation.
Our digital intervention prototype (called 'ADvisor') was developed on the basis of a planning phase consisting of qualitative and quantitative reviews, an in-depth qualitative study, the development of guiding principles and a theory-based behavioural analysis. Our optimisation phase consisted of 'think aloud' interviews where the intervention was iteratively refined.
The qualitative systematic review and in-depth qualitative study highlighted the centrality of fear of depression relapse as a key barrier to discontinuation. The quantitative systematic review showed that psychologically informed approaches such as cognitive-behavioural therapy were associated with greater rates of discontinuation than simple advice to reduce. Following a behavioural diagnosis based on the behaviour change wheel, social cognitive theory provided a theoretical basis for the intervention. The intervention was optimised on the basis of think aloud interviews, where participants suggested they like the flexibility of the system and found it reassuring. Changes were made to the tone of the material and the structure was adjusted based on this qualitative feedback.
'ADvisor' is a theory, evidence and person-based digital intervention designed to support antidepressant discontinuation. The intervention was perceived as helpful and reassuring in optimisation interviews. Trials are now needed to determine the feasibility, clinical and cost-effectiveness of this approach.
我们旨在开发一种数字干预措施，以支持英国初级保健中的抗抑郁药停药，该措施具有可扩展性、可及性、安全性和可行性。本文介绍了使用理论、证据和以人为本的方法进行开发的情况。
使用理论、证据和以人为本的方法进行干预措施的开发。
英格兰南部的初级保健。
十五名具有不同抗抑郁药经验的参与者参加了“出声思考”访谈，以优化干预措施。
我们的数字干预原型（称为“ADvisor”）是基于计划阶段开发的，该阶段包括定性和定量审查、深入的定性研究、指导原则的制定和基于理论的行为分析。我们的优化阶段包括“出声思考”访谈，在此过程中，干预措施被反复改进。
定性系统评价和深入的定性研究强调了对抑郁复发的恐惧作为停药的关键障碍的中心地位。定量系统评价表明，心理信息方法，如认知行为疗法，与更高的停药率相关，而不仅仅是简单的减少建议。基于行为变化轮的行为诊断，社会认知理论为干预措施提供了理论基础。基于出声思考访谈，对干预措施进行了优化，参与者表示他们喜欢系统的灵活性，并感到安心。根据这一定性反馈，对材料的语气和结构进行了更改。
“ADvisor”是一种基于理论、证据和以人为本的数字干预措施，旨在支持抗抑郁药停药。在优化访谈中，该干预措施被认为是有帮助和令人安心的。现在需要进行试验来确定这种方法的可行性、临床效果和成本效益。
