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确定系统性红斑狼疮中健康的有意义方面和评估感兴趣的概念:对数字临床测量发展的影响。

Identifying meaningful aspects of health and concepts of interest for assessment in systemic lupus erythematosus: implications for digital clinical measure development.

作者信息

Kamudoni Paul, Lyden Kate, Günther Oliver, Jaitely Vikas, Araujo Thiago Dantas, Spies Erica, Park Josephine, Thomas Erik, Buie Joy, Blankenship Jennifer M, Arnaud Laurent

机构信息

Clinical Measurement Sciences, The Healthcare Business of Merck KGaA, Darmstadt, Germany.

VivoSense, Newport Coast, CA, USA.

出版信息

J Patient Rep Outcomes. 2024 Dec 24;8(1):154. doi: 10.1186/s41687-024-00832-7.

DOI:10.1186/s41687-024-00832-7
PMID:39718677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11668707/
Abstract

OBJECTIVES

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with heterogeneous clinical manifestations which significantly impacts the daily lives of patients. Herein, we aimed to (i) investigate patients' perspectives on and experience with SLE; (ii) identify meaningful aspects of health (MAHs) and concepts of interest (COIs) in SLE that could be evaluated using digital clinical measures (DCMs); and (iii) identify target DCMs for their assessment.

METHODS

A mixed-methods, multistep approach was deployed for (i) exploring patients' experience with SLE through a social media listening study and focused group discussions with patients; (ii) mapping patients' experiences to define MAHs and identify COIs measurable using DCMs; (iii) selecting DCMs for the target COIs; and (iv) identifying types of wearable sensors for measuring COIs in the patients.

RESULTS

Six MAHs related to physical behavior and sleep were identified: difficulty in ambulating, lack of energy, inability to perform activities of daily living, difficulty engaging in sustained walking, inability to perform leisure activities and exercise, and lack of restful sleep. Measurable COIs represented walking (fatigue and pain) and sleep (sleep and pain) characteristics. Five and six DCMs related to stepping behavior and sleep quality, respectively, were identified. Several wearable sensors are available to derive DCMs for physical behavior and sleep; however, patients showed a strong preference for a wrist-worn actigraphy sensor.

CONCLUSION

We identified DCMs for physical behavior and sleep that are relevant and meaningful to patients with SLE, measurable in a real-world environment with wearable sensors, and have the potential to aid personalized patient care.

摘要

目的

系统性红斑狼疮(SLE)是一种临床表现多样的慢性自身免疫性疾病,严重影响患者的日常生活。在此,我们旨在:(i)调查患者对SLE的看法和体验;(ii)确定SLE中可通过数字临床测量(DCM)评估的健康有意义方面(MAH)和感兴趣概念(COI);(iii)确定用于评估的目标DCM。

方法

采用混合方法、多步骤方法进行:(i)通过社交媒体倾听研究和与患者的焦点小组讨论探索患者的SLE体验;(ii)将患者的体验进行映射以定义MAH并确定可通过DCM测量的COI;(iii)为目标COI选择DCM;(iv)确定用于测量患者COI的可穿戴传感器类型。

结果

确定了与身体行为和睡眠相关的六个MAH:行走困难、缺乏能量、无法进行日常生活活动、难以进行持续行走、无法进行休闲活动和锻炼以及缺乏安稳睡眠。可测量的COI代表行走(疲劳和疼痛)和睡眠(睡眠和疼痛)特征。分别确定了与步行动为和睡眠质量相关的五个和六个DCM。有几种可穿戴传感器可用于获取身体行为和睡眠的DCM;然而,患者对腕戴式活动记录仪传感器表现出强烈偏好。

结论

我们确定了对SLE患者相关且有意义的身体行为和睡眠的DCM,可在现实环境中通过可穿戴传感器进行测量,并且有可能有助于个性化患者护理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d8/11668707/b9debca8a27f/41687_2024_832_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d8/11668707/b39daab5be51/41687_2024_832_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d8/11668707/b9debca8a27f/41687_2024_832_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d8/11668707/b39daab5be51/41687_2024_832_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d8/11668707/b9debca8a27f/41687_2024_832_Fig2_HTML.jpg

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本文引用的文献

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JMIR Form Res. 2024 Feb 2;8:e52768. doi: 10.2196/52768.
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Psychometric properties of performance-based measures of physical function administered via telehealth among people with chronic conditions: A systematic review.基于性能的远程医疗下慢性疾病患者身体功能测量的心理测量学特性:系统综述。
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The Patient Experience of Fatigue in Systemic Lupus Erythematosus: A Conceptual Model.
系统性红斑狼疮患者疲劳的体验:一个概念模型
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Development of a digital toolkit to improve quality of life of patients with systemic lupus erythematosus.开发一种数字工具包以改善系统性红斑狼疮患者的生活质量。
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Wearable sensors enable personalized predictions of clinical laboratory measurements.可穿戴传感器可实现临床实验室测量的个性化预测。
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