4.6 Article

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

Journal

BMJ OPEN
Volume 13, Issue 5, Pages -

Publisher

BMJ PUBLISHING GROUP
DOI: 10.1136/bmjopen-2022-068756

Keywords

NEUROLOGY; Neurological injury; GERIATRIC MEDICINE; TRAUMA MANAGEMENT

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The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially, but research on TBI in this population is sparse. A home monitoring system called Minder, developed by the UK Dementia Research Institute, will be used to passively collect sleep and activity data from older adults with TBI. The study aims to assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.
IntroductionThe 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.Methods and analysisThe 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.Ethics and disseminationEthical 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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