针对职业压力的移动健康干预个性化：一项随机试点研究方案。

Personalizing mHealth Interventions for Occupational Stress: Protocol for a Randomized Pilot Study.

作者信息

Kunas Beatrix, Jung Oliver, Schranz Christoph, Schmoigl-Tonis Mathias, Ploessnig Manuela, Laireiter Anton-Rupert

机构信息

Department of Psychology, Paris-Lodron University Salzburg, Salzburg, Austria.

Salzburg Research Forschungsgesellschaft mbH, Salzburg, Austria.

出版信息

JMIR Res Protoc. 2025 Jun 3;14:e68012. doi: 10.2196/68012.

DOI:10.2196/68012

PMID:40460419

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12174872/

Abstract

BACKGROUND

Occupational stress is associated with detrimental consequences that are addressed by mobile health (mHealth) solutions. Previous developments of apps for occupational stress have not yet fully exploited the potential of multilevel diagnostics through the integration of wearable sensors for interventions. Personalizing mHealth approaches in terms of intervention time and content, which requires the use of artificial intelligence, is the next logical developmental step. The "Relax" approach developed a corresponding prototype of an app-wearable system, which will be evaluated for effectiveness in terms of stress reduction and usability.

OBJECTIVE

This study protocol describes an evaluation study used to test the effectiveness and usability of the Relax approach.

METHODS

The evaluation study was designed as a 2-arm randomized trial with 2 phases, each with a 3-week intervention period. In both phases, employees were required to use the app to record daily stress and to wear a wearable sensor to measure heart rate variability. The app offered interventions based on algorithms, which altered the probability of their selection after learning from the data, thereby personalizing the user experience. In the second phase of the study, the sample was divided into 2 groups, varying the degree of personalization of the app. To analyze effectiveness, a 2-factorial mixed within-between design will be applied to compare the outcomes between both groups as well as in a pre-post comparison. In addition, exploratory analyses of the usability of the approach are planned.

RESULTS

The study was conducted during the spring and summer of 2024, with a total of 46 participants enrolled, and is ready for data analysis.

CONCLUSIONS

The Relax approach, including a number of factors related to personalization that have not yet been incorporated into mHealth in current research, will provide new insights into the next steps of advanced mHealth solutions. Limitations of the study design, such as the lack of a control group and the sample representativity, have to be addressed.

TRIAL REGISTRATION

Open Science Foundation 10.17605/OSF.IO/MYRD9; https://osf.io/myrd9.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68012.

摘要

背景

职业压力会带来有害后果，移动健康（mHealth）解决方案可应对这些后果。此前开发的职业压力应用程序尚未通过整合可穿戴传感器进行干预来充分挖掘多级诊断的潜力。在干预时间和内容方面实现移动健康方法的个性化，这需要使用人工智能，是下一步合理的发展方向。“放松”方法开发了一个相应的应用程序-可穿戴系统原型，将对其在减轻压力和可用性方面的有效性进行评估。

目的

本研究方案描述了一项用于测试“放松”方法有效性和可用性的评估研究。

方法

评估研究设计为一项两臂随机试验，分为两个阶段，每个阶段有3周的干预期。在两个阶段中，员工都被要求使用该应用程序记录每日压力，并佩戴可穿戴传感器测量心率变异性。该应用程序基于算法提供干预措施，这些干预措施在从数据中学习后会改变其被选中的概率，从而实现用户体验的个性化。在研究的第二阶段，样本被分为两组，应用程序的个性化程度有所不同。为了分析有效性，将采用二因素混合组内-组间设计来比较两组之间以及前后比较的结果。此外，还计划对该方法的可用性进行探索性分析。