用户旅程法：一项关于改进数字干预使用测量的案例研究。

User journey method: a case study for improving digital intervention use measurement.

作者信息

Lukka Lauri, Vesterinen Maria, Salonen Antti, Bergman Vilma-Reetta, Torkki Paulus, Palva Satu, Palva J Matias

机构信息

Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Rakentajanaukio 2, Espoo, 02150, Finland.

Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.

出版信息

BMC Health Serv Res. 2025 Apr 1;25(1):479. doi: 10.1186/s12913-025-12641-9.

DOI:10.1186/s12913-025-12641-9

PMID:40165237

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11959768/

Abstract

BACKGROUND

Many digital mental health interventions meet low levels of use. However, current use measurement methods do not necessarily help identify which intervention elements are associated with dropout, despite this information potentially facilitating iterative intervention development. Here, we suggest improving the comprehensiveness of intervention use measurement with the user journey method, which evaluates every intervention element to identify intervention-specific use barriers.

METHODS

We applied user journey method in a clinical trial that investigated the efficacy of a novel game-based intervention, Meliora, for adult Major Depressive Disorder. We modelled the intervention for its four technological (Recruitment, Website, Questionnaires, Intervention Software) and two interpersonal elements (Assessment, Support). We then applied the user journey method to measure how many users proceeded from one element to the next combining social media analytics, website use data, signup data, clinical subject coordinator interview data, symptom questionnaire data, and behavioral intervention use data. These measurements were complemented with the qualitative analysis of the study discovery sources and email support contacts.

RESULTS

Recruitment: The intervention recruitment reached at least 145,000 Finns, with social media, word-of-mouth, and news and web sources being the most effective recruitment channels. Website: The study website received 16,243 visitors, which led to 1,007 sign-ups.

ASSESSMENT

895 participants were assessed and 735 were accepted. Intervention Software: 498 participants were assigned to the active intervention or comparator, of whom 457 used them at least once: on average, for 17.3 h (SD = 20.4 h) on 19.7 days (SD = 20.7 d) over a period of 38.9 days (SD = 31.2 d). The 28 intervention levels were associated with an average dropout rate of 2.6%, with two sections exhibiting an increase against this baseline. 150 participants met the minimum adherence goal of 24 h use. Questionnaires: 116 participants completed the post-intervention questionnaire.

SUPPORT

313 signed-up participants contacted the researchers via email.

CONCLUSION

The user journey method allowed for the comprehensive evaluation of the six intervention elements, and enabled identifying use barriers expediting iterative intervention development and implementation.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05426265. Registered 28 June 2022, https://clinicaltrials.gov/ct2/show/NCT05426265 .

摘要

背景

许多数字心理健康干预措施的使用率较低。然而，尽管这些信息可能有助于迭代干预措施的开发，但目前的使用测量方法不一定有助于确定哪些干预因素与退出有关。在此，我们建议使用用户旅程法来提高干预措施使用测量的全面性，该方法评估每个干预因素以识别特定于干预措施的使用障碍。

方法

我们在一项临床试验中应用了用户旅程法，该试验调查了一种新型基于游戏的干预措施Meliora对成人重度抑郁症的疗效。我们对该干预措施的四个技术要素（招募、网站、问卷、干预软件）和两个人际要素（评估、支持）进行了建模。然后，我们应用用户旅程法，结合社交媒体分析、网站使用数据、注册数据、临床项目协调员访谈数据、症状问卷数据和行为干预使用数据，来测量有多少用户从一个要素进入到下一个要素。这些测量通过对研究发现来源和电子邮件支持联系人的定性分析得到补充。

结果

招募：该干预措施的招募人数至少达到145,000名芬兰人，社交媒体、口碑以及新闻和网络来源是最有效的招募渠道。网站：研究网站有16,243名访客，其中1,007人注册。

评估

895名参与者接受了评估，735人被接受。干预软件：498名参与者被分配到积极干预组或对照组，其中457人至少使用过一次：平均使用时长为17.3小时（标准差=20.4小时），使用天数为19.7天（标准差=20.7天），为期38.9天（标准差=31.2天）。28个干预级别对应的平均退出率为2.6%，有两个部分的退出率高于此基线。150名参与者达到了24小时使用时长的最低依从目标。问卷：116名参与者完成了干预后问卷。