Evidence map›Paper›PMID 37217265›Full record

ArticleBMJ open2023

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

Megan E Parkinson, Melanie Dani, Michael Fertleman, Eyal Soreq, Payam Barnaghi, David J Sharp, Lucia M Li, UK Dementia Research Institute Care Research & Technology Research Group

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Megan E ParkinsonBioengineering, Imperial College London, London, UK.ORCID 0000-0003-4926-9329
Melanie DaniBioengineering, Imperial College London, London, UK.
Michael FertlemanBioengineering, Imperial College London, London, UK.ORCID 0000-0003-4023-1156
Eyal SoreqUK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK.
Payam BarnaghiUK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK.ORCID 0000-0001-8591-9638
David J SharpUK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK.
Lucia M LiUK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK lucia.li@imperial.ac.uk.
UK Dementia Research Institute Care Research & Technology Research Group
UK Dementia Research Institute · GBImperial College London · GB

Funding

Department of Health
6 · The paper itself

Abstract

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 ANALYSIS: 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. ETHICS AND DISSEMINATION: 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.

Indexed as

Brain Injuries, TraumaticDementiaAgedCaregiversFeasibility StudiesHumansMultimorbidityGERIATRIC MEDICINENeurological injuryNEUROLOGYTRAUMA MANAGEMENT

Identifiers

PMID37217265
PMCPMC10230906
OpenAlexW4377289949

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.