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基于移动应用的 TEAM-CBT(测试共情评估方法-认知行为疗法)干预(感觉良好)治疗抑郁症的可行性和可接受性:二次数据分析。

Feasibility and Acceptability of a Mobile App-Based TEAM-CBT (Testing Empathy Assessment Methods-Cognitive Behavioral Therapy) Intervention (Feeling Good) for Depression: Secondary Data Analysis.

机构信息

PGSP-Stanford PsyD. Consortium, Palo Alto, CA, United States.

Stanford School of Medicine Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States.

出版信息

JMIR Ment Health. 2024 May 10;11:e52369. doi: 10.2196/52369.

DOI:10.2196/52369
PMID:38728080
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11127134/
Abstract

BACKGROUND

The Feeling Good App is an automated stand-alone digital mobile mental health tool currently undergoing beta testing with the goal of providing evidence-informed self-help lessons and exercises to help individuals reduce depressive symptoms without guidance from a mental health provider. Users work through intensive basic training (IBT) and ongoing training models that provide education regarding cognitive behavioral therapy principles from a smartphone.

OBJECTIVE

The key objective of this study was to perform a nonsponsored third-party academic assessment of an industry-generated data set; this data set focused on the safety, feasibility, and accessibility of a commercial automated digital mobile mental health app that was developed to reduce feelings associated with depression.

METHODS

The Feeling Good App development team created a waitlist cohort crossover design and measured symptoms of depression and anxiety using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and an app-specific measure of negative feelings called the 7 Dimension Emotion Slider (7-DES). The waitlist cohort crossover design divided the participants into 2 groups, where 48.6% (141/290) of the participants were given immediate access to the apps, while 51.4% (149/290) were placed on a 2-week waitlist before being given access to the app. Data collected by the Feeling Good App development team were deidentified and provided to the authors of this paper for analysis through a nonsponsored university data use agreement. All quantitative data were analyzed using SPSS Statistics (version 28.0; IBM Corp). Descriptive statistics were calculated for demographic variables. Feasibility and acceptability were descriptively assessed. All participants included in the quantitative data were given access to the Feeling Good App; this study did not include a control group.

RESULTS

In terms of safety, there was no statistically significant change in suicidality from preintervention to postintervention time points (t=0.0; P>.99), and there was a statistically significant decrease in hopelessness from preintervention to postintervention time points (F=30.16; P<.01). In terms of acceptability, 72.2% (166/230) of the users who started the initial 2-day IBT went on to complete it, while 34.8% (80/230) of the users who started IBT completed the entirety of the apps' 4-week protocol (150/230, 65.22% dropout rate over 4 weeks).

CONCLUSIONS

This study is the first reported proof-of-concept evaluation of the Feeling Good App in terms of safety, feasibility, and statistical trends within the data set. It demonstrates a feasible and novel approach to industry and academic collaboration in the process of developing a digital mental health technology translated from an existing evidence-informed treatment. The results support the prototype app as safe for a select nonclinical population. The app had acceptable levels of engagement and dropouts throughout the intervention. Those who stay engaged showed reductions in symptom severity of depression warranting further investigation of the app's efficacy.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/19f004515d7d/mental_v11i1e52369_fig10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/0f736731a004/mental_v11i1e52369_fig1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/8b0d1cc10306/mental_v11i1e52369_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/d3917842e5d7/mental_v11i1e52369_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/777448366f22/mental_v11i1e52369_fig5.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/b348f4ef51f3/mental_v11i1e52369_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/3a870a206d0f/mental_v11i1e52369_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cc1/11127134/19f004515d7d/mental_v11i1e52369_fig10.jpg
摘要

背景

Feeling Good App 是一款自动化的独立数字移动心理健康工具,目前正在进行测试,旨在提供基于证据的自助课程和练习,帮助个人减轻抑郁症状,而无需心理健康提供者的指导。用户通过强化基础培训(IBT)和持续培训模型来使用智能手机了解认知行为疗法原则。

目的

本研究的主要目的是对行业生成的数据集进行非赞助的第三方学术评估;该数据集重点关注一款商业自动化数字移动心理健康应用程序的安全性、可行性和可及性,该应用程序旨在减轻与抑郁相关的感觉。

方法

Feeling Good App 开发团队创建了一个候补队列交叉设计,并使用患者健康问卷-9、广泛性焦虑症-7 和应用程序特定的负面情绪测量工具 7 维情绪滑块(7-DES)来测量抑郁和焦虑症状。候补队列交叉设计将参与者分为两组,其中 48.6%(141/290)的参与者立即获得应用程序的访问权限,而 51.4%(149/290)的参与者在获得应用程序访问权限之前被安排在为期两周的候补名单上。Feeling Good App 开发团队收集的数据经过去识别,并通过非赞助的大学数据使用协议提供给本文作者进行分析。所有定量数据均使用 SPSS Statistics(版本 28.0;IBM Corp)进行分析。对人口统计学变量进行描述性统计。描述性评估了可行性和可接受性。所有纳入定量数据分析的参与者都可以访问 Feeling Good App;本研究没有包括对照组。

结果

在安全性方面,从干预前到干预后时间点,自杀意念没有统计学上的显著变化(t=0.0;P>.99),而从干预前到干预后时间点,绝望感有统计学上的显著下降(F=30.16;P<.01)。在可接受性方面,72.2%(166/230)开始初始 2 天 IBT 的用户继续完成,而 34.8%(80/230)开始 IBT 的用户完成了应用程序 4 周方案的全部内容(150/230,4 周内的辍学率为 65.22%)。

结论

这是首次报告关于 Feeling Good App 在安全性、可行性和数据集中的统计趋势方面的概念验证评估。它展示了在将现有循证治疗转化为数字心理健康技术的过程中,行业和学术合作的一种可行且新颖的方法。结果支持该原型应用程序对特定非临床人群是安全的。该应用程序在整个干预过程中具有可接受的参与度和辍学率。那些保持参与的人表现出抑郁症状严重程度的减轻,值得进一步研究该应用程序的疗效。

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