Characterizing the Complexity of Advanced Cancer Pain in the Home Context by Leveraging Smart Health Technology

利用智能健康技术表征家庭中晚期癌症疼痛的复杂性

• 批准号: 10096693
• 负责人: Virginia Townsend LeBaron
• 金额: $ 70.43万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10096693
• 关键词: Accident and Emergency department; Acute Pain; Address; Adherence; Advanced Malignant Neoplasm; Affect; Behavioral; Breakthrough Pain; Cancer Intervention; Cancer Pain Management; Cancer Patient; Caregivers; Caring; Clinic; Complex; Custom; Data; Disease; Disease Progression; Effectiveness; Emotional; Environmental Risk Factor; Event; Family; Family Caregiver; Fingerprint; Frequencies; Health; Health Personnel; Health Technology; Health system; Healthcare; Healthcare Systems; Home; Hospice Programs; Individual; Interdisciplinary Study; Intervention; Life; Malignant Neoplasms; Monitor; Moods; Observational Study; Oncology; Opioid; Outpatients; Pain; Palliative Care; Patient Care; Patient Self-Report; Patients; Persons; Pharmaceutical Preparations; Pharmacology; Phenotype; Physiological; Play; Quality of life; Reporting; Research; Role; SMART health; Signs and Symptoms; Sleep; Source; Symptoms; System; Technology; Time; Wireless Technology; Work; advanced disease; cancer pain; contextual factors; coping; data sharing; data visualization; design; digital; end of life; evidence base; experience; improved; individual patient; innovation; minimally invasive; mobile computing; negative affect; novel; opioid epidemic; personalized management; personalized strategies; prediction algorithm; preference; psychologic; recruit; sensor; smart watch; social; symptom management; symptomatic improvement; treatment effect

Project Summary Cancer pain is complex, prevalent and has serious consequences for patients, family caregivers and healthcare systems. Inadequately managed cancer pain can be particularly problematic for patients coping with advanced, metastatic disease. Most symptom management occurs in the home setting, and family caregivers often play a key role in helping to manage cancer pain, but often find this task daunting and stressful. Complicating cancer pain management is the reality that opioids, a main-stay class of medications used to control serious cancer pain, are subject to increased scrutiny given well-publicized concerns about the national `opioid epidemic.' Now more than ever, it is imperative we understand how patients and family caregivers attempt to manage cancer pain at home so we can offer them personalized support to effectively and safely alleviate pain. Mobile and wireless technology (`smart health') can help support symptom management in the home setting, but must be carefully designed to account for the realities of patients and family caregivers coping with advanced disease. We hypothesize that individuals, and patient-family caregiver dyads, will display a unique `digital fingerprint' (or phenotype) of the advanced cancer pain experience – that if better understood can be utilized to inform and deliver personalized, timely interventions. The purpose of this study, which builds upon preliminary pilot work, is to deploy an unobtrusive smart health system, the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), to monitor and describe – and ultimately to predict and help manage – the experience of advanced cancer pain in the home setting. BESI-C is comprised of wearable (smart watch) and environmental sensors that collect physiological, behavioral, and contextual data at the individual, dyad and home level that can be integrated to provide a comprehensive picture of a health related phenomenon. A unique feature of the BESI-C system is the ability of patients and caregivers to record and characterize cancer pain events from their own perspective using a custom application on their respective smart watch. Specifically, this observational research will analyze data collected via BESI-C from patient-family caregiver dyads recruited from an outpatient oncology palliative care clinic and a home hospice program, to develop comprehensive `digital phenotypes' of advanced cancer pain in the home setting. These digital phenotypes will characterize the frequency, intensity and impact on quality of life of pain events; monitor the use of pharmacological and non-pharmacological strategies and self-reported effectiveness; correlate environmental, contextual, behavioral and physiological sensor data with reported pain events; and evaluate concordance of patient and caregiver data. This research will also explore preferences for communicating collected data with patients, family caregivers and healthcare providers by creating and sharing data visualizations. Additionally, we will explore which sensing data are most predictive of breakthrough pain events to build parsimonious pain prediction algorithms.

项目摘要 癌症疼痛复杂、普遍，对患者、家庭护理人员和 医疗保健系统。不适当的癌症疼痛管理可能是特别有问题的病人应对 患有晚期转移性疾病大多数症状管理发生在家庭环境中， 护理人员通常在帮助管理癌症疼痛方面发挥关键作用，但通常发现这项任务令人生畏， 压力很大复杂的癌症疼痛管理是现实，阿片类药物，一个主要的停留类药物 用于控制严重的癌症疼痛，受到越来越多的审查，考虑到广泛宣传的关注， 全国性的阿片类药物流行病。“现在比以往任何时候都更重要的是，我们必须了解患者和家人如何 护理人员试图在家中管理癌症疼痛，因此我们可以为他们提供个性化的支持， 并安全地减轻疼痛。移动的和无线技术（“智能健康”）可帮助支持症状 在家庭环境中的管理，但必须仔细设计，以考虑病人的现实情况， 应对晚期疾病的家庭照顾者。我们假设，个人和病人家庭照顾者 二分体，将显示一个独特的“数字指纹”（或表型）的先进的癌症疼痛的经验-如果 更好地理解可以用来提供信息和提供个性化的，及时的干预。这样做的目的 这项研究建立在初步试点工作的基础上，旨在部署一个不引人注目的智能卫生系统， 癌症行为和环境感知与干预（BESI-C），监测和描述-以及 最终预测和帮助管理-在家庭环境中晚期癌症疼痛的经验。BESI-C是 由可穿戴（智能手表）和环境传感器组成，这些传感器收集生理、行为和 个人、二元组和家庭层面的背景数据，可以整合起来， 这是一个与健康有关的现象。BESI-C系统的一个独特功能是患者的能力， 护理人员使用自定义工具从自己的角度记录和描述癌痛事件， 在各自的智能手表上。具体来说，这项观察性研究将分析收集到的数据， 通过BESI-C，从门诊肿瘤姑息治疗诊所招募的患者-家庭护理人员二人组中， 家庭临终关怀计划，在家中开发晚期癌症疼痛的综合“数字表型” 设置.这些数字表型将表征疼痛的频率、强度和对生活质量的影响 事件;监测药理学和非药理学策略的使用，并自我报告 有效性;将环境、情境、行为和生理传感器数据与报告的疼痛相关联 事件;并评估患者和护理人员数据的一致性。这项研究还将探讨偏好 用于通过创建和 共享数据可视化。此外，我们将探讨哪些传感数据最具预测性 突破性的疼痛事件来构建简约的疼痛预测算法。