Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study.
Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study.
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DOI：
10.1136/bmjopen-2022-068756
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发表时间：
2023-05-22
期刊：
影响因子：
2.9
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中科院分区：
文献类型：
--
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老年人创伤性脑损伤（TBI）的患病率呈指数级增长。其在老年人中造成的后遗症可能很严重，并且会与多种疾病共存等年龄相关的状况相互影响。尽管如此，针对老年人的TBI研究却很少。由英国痴呆症研究学院护理研究与技术中心开发的Minder家庭监测系统，利用红外传感器和床垫被动收集睡眠和活动数据。类似的系统已被用于监测患有痴呆症的老年人的健康状况。我们将评估使用该系统研究老年人创伤性脑损伤后早期健康状况变化的可行性。
这项研究将招募15名60岁以上患有中重度创伤性脑损伤的住院患者，在6个月的时间里，使用被动式和可穿戴式传感器监测他们的日常活动和睡眠模式。参与者将在每周的电话回访中报告自身健康状况，这些信息将用于验证传感器数据。在整个研究期间，将进行身体、功能和认知评估。从传感器数据中得出的活动水平和睡眠模式将通过活动图进行计算和可视化呈现。将进行参与者内部分析，以确定参与者是否偏离了自己的日常规律。我们将把机器学习方法应用于活动和睡眠数据，以评估这些数据的变化是否能够预测临床事件。对参与者、护理人员和临床工作人员进行的访谈进行定性分析，以评估该系统的可接受性和实用性。
这项研究已获得伦敦 - 坎伯韦尔圣贾尔斯研究伦理委员会（REC）的伦理批准（REC编号：17/LO/2066）。研究结果将提交给同行评审期刊发表，在会议上展示，并为一项更大规模的创伤性脑损伤后康复评估试验的设计提供依据。
The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI. The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system. Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI.
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