数字表型在了解正念应用程序参与模式方面的潜力:试点研究。

IF 1.3 4区 医学 Q3 INTEGRATIVE & COMPLEMENTARY MEDICINE Journal of Integrative and Complementary Medicine Pub Date : 2024-06-05 DOI:10.1089/jicm.2023.0698
Lucy Gray, Natalia Marcynikola, Ian Barnett, John Torous
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引用次数: 0

摘要

背景:应用程序参与度低是数字心理健康功效的主要障碍。随着基于正念的心理健康应用程序越来越受欢迎,我们需要对影响参与度的因素有新的认识。本研究利用数字表型来了解基于应用程序的正念的实时参与模式。研究提出了不同的参与度指标,衡量参与者每周完成的基于应用程序的活动总数,以及参与者每周使用应用程序的天数比例。方法:数据来源于一项为期四周的研究的两次迭代,该研究探讨了大学生(n = 169)的应用程序参与度。这项二次分析调查了一般和正念应用程序参与度与每周被动数据指标(睡眠时长、在家时间和屏幕时长)之间的关系,以及人口统计学与参与度之间的关系。此外,还对正念活动完成度较高的三位参与者进行了临床重点分析。研究结果性别、种族/民族和年龄等人口统计学变量与正念应用程序的参与度之间没有显著关联。被动数据变量(如睡眠和屏幕持续时间)对每周的一般和正念应用程序参与度的不同指标具有显著的预测作用。完成正念活动的数量与参与者是否收到正念通知之间的屏幕持续时间存在明显的交互效应。使用被动数据特征来预测正念活动完成情况的 K-means 聚类分析效果不佳。结论:虽然没有简单的解决方案来预测正念应用程序的参与度,但在人群和个人层面利用数字表型方法提供了新的潜力。来自数字表型的信号值得进行更多研究;考虑到正念应用程序的参与率已经很高,其可用性和吸引不同人群患者参与的潜力,即使是正念应用程序参与率的微小增长也可能产生巨大影响。
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The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study.

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

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