Seehausen Aaron, Chodan Wencke, Höpfner Florian, Schneider Carolin, Felser Sabine, Murua Escobar Hugo, Aehnelt Mario, Junghanss Christian
Clinic III - Hematology, Oncology, Palliative Medicine, Rostock University Medical Centre, Rostock, Germany.
Fraunhofer Institute for Computer Graphics Research, Rostock, Germany.
JMIR Res Protoc. 2025 Jul 16;14:e63549. doi: 10.2196/63549.
Nursing in palliative medicine combines primary patient care with the special challenges of this medical field (eg, handling the processes of dying, grief, and death). These cause high stress levels and burden on the nursing staff, resulting in an early exit from working life because of physical or psychological disorders like burnout.
DiPa (digitally identifying and minimizing stressors in palliative care) is a prospective study investigating the feasibility of measuring burden and its causes in palliative care using methods of subjective and objective stress detection. Based on these results, stress-reducing interventions are to be deduced and evaluated. In this paper, we present our study protocol.
The nursing staff of an inpatient university palliative hospital ward gathered data over 6 weeks. Each was equipped with a smart wristband and a smartphone that continuously measure physiological and ambient parameters throughout their working day. These objective data were enriched by subjective measurements: a questionnaire at the beginning of the study that assessed multiple potential stressful situations and constellations in the private and working environment as well as ecological momentary assessments (EMAs) during the workday. The EMAs were prompted by scanning near-field communication (NFC) tags placed at different locations on the ward. The ongoing data analyses will be processed using computer algorithms partly programmed specifically for this study and partly drawn from existing libraries, such as toolboxes for neurophysiological signal processing for Python. Comparisons between subjective and objective measures and group comparisons between variables of interest will be made using inferential statistics, including regression analyses and analyses of variance. Data analysis using machine learning algorithms will be implemented once sufficient data are gathered.
The study was funded in October 2019. As of July 2025, 12 of 18 nurses in the palliative care unit consented to participate in our study. We expect to start detailed data analysis in in the third quarter of 2025 and to finish and publish our results in 2026.
The DiPa study aims at testing the feasibility of measuring and merging subjective and objective stress parameters for palliative care nurses.
German Register for Clinical Studies DRKS00024425; https://drks.de/search/en/trial/DRKS00024425/details.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63549.
姑息医学护理将患者的基础护理与该医学领域的特殊挑战相结合（例如，处理死亡、悲伤和临终过程）。这些给护理人员带来了高度的压力和负担，导致他们因身体或心理障碍（如职业倦怠）而提前结束职业生涯。
DiPa（姑息治疗中数字识别和最小化压力源）是一项前瞻性研究，旨在调查使用主观和客观压力检测方法来测量姑息治疗中的负担及其原因的可行性。基于这些结果，推导出并评估减轻压力的干预措施。在本文中，我们展示了我们的研究方案。
一家大学住院姑息医院病房的护理人员在6周内收集数据。每人配备一个智能手环和一部智能手机，在整个工作日持续测量生理和环境参数。这些客观数据通过主观测量得到补充：研究开始时的一份问卷，评估私人和工作环境中多种潜在的压力情况和组合，以及工作日期间的生态瞬时评估（EMA）。EMA通过扫描放置在病房不同位置的近场通信（NFC）标签来触发。正在进行的数据分析将使用部分专门为此研究编写程序、部分从现有库（如用于Python的神经生理信号处理工具箱）中提取的计算机算法进行处理。将使用包括回归分析和方差分析在内的推断统计方法对主观和客观测量之间进行比较，以及对感兴趣的变量进行组间比较。一旦收集到足够的数据，将使用机器学习算法进行数据分析。
该研究于2019年10月获得资助。截至2025年7月，姑息治疗病房的18名护士中有12名同意参与我们的研究。我们预计在2025年第三季度开始详细的数据分析，并在2026年完成并公布我们的结果。
DiPa研究旨在测试测量和合并姑息治疗护士主观和客观压力参数的可行性。
德国临床研究注册中心DRKS00024425；https://drks.de/search/en/trial/DRKS00024425/details。
国际注册报告识别码（IRRID）：DERR1-10.2196/63549。
