Abstract

Abstract Introduction Major trauma including Traumatic Brain Injury (TBI) is an increasingly common cause of hospitalisation in older adults (OAs). We studied post-discharge recovery from TBI using a remote healthcare monitoring system that captures data on activity and sleep. We aim to assess the feasibility and acceptability of this technology to monitor recovery at home following a significant acute health event in OAs. Methods We installed Minder, a remote healthcare monitoring system, in recently discharged patients >60 years with moderate-severe TBI. We present descriptive analyses of post-discharge recovery for two males, corroborating data from Minder against verified activities and events. We recorded semi-structured interviews assessing acceptability. Both participants have similar household set-up, multimorbidity profiles and clinical frailty scores; however, one participant has prior cognitive impairment (PAT1), and one does not (PAT2). Results We present 10 weeks of sleep and activity data from Minder and feedback from interviews. Data observed from PAT1 revealed habitual patterns of activity and sleep. These remained stable, despite discrete clinical events. Conversely, PAT2's data revealed irregular sleep patterns that became increasingly fragmented. Activity was detected in multiple rooms throughout the house at night, consistent with carer reports of night-time wandering. Increased overnight activity coincided with multiple falls, prompting increased care provision. Initial feedback from interviews was the technology helped participants and those involved in their care feel supported. Conclusions As pressure on services mounts, novel approaches to post-discharge care are of increasing importance. Remote healthcare monitoring can provide high temporal resolution data offering ‘real world’ insights into the effects of significant health events in OAs. Our provisional results support our hypothesis that use of this technology is feasible and acceptable for frail, multimorbid participants and highlights the substantial potential of this technology to help clinicians improve community-based care and more effectively monitor interventions and chronic conditions.

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