A classification system for delirium subtyping with the use of a commercial mobility monitor

Godfrey, Alan; Conway, Richard; Leonard, Maeve; Meagher, David; ÓLaighin, Gearóid

doi:10.1016/j.gaitpost.2009.05.014

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…[13][14][15][16]18 These five studies differed in the statistical methodology that was used to identify delirium subtypes on the basis of the activity data. A pilot study for these five studies, with a subset of three of the 34 patients, is described in Leonard et al 17 In Tables 3 and 4, we only show the details of Godfrey et al, 18 because most of the details of the other studies are exactly the same, except for the statistical framework. We also note that the study population of Osse et al 19,20 is the same.…”

Section: Results Of Systematic Reviewmentioning

confidence: 99%

“…In addition, items concerning psychomotor activation and retardation were used. 6 In the third study on this topic, 34 palliative care patients were monitored with the activPAL, 18 a discrete accelerometer-based device, 15,16 or both. 13,14,17 The data were used to build a so-called ''classifier,'' a software tool that is able to classify a patient based on his or her activity data in one of the subgroups of hyperactive, hypoactive or mixed.…”

Section: Results Of Systematic Reviewmentioning

confidence: 99%

“…Three papers 18,21,23 addressed the question of whether a measurement of the 24-hr motor activity pattern could be helpful to determine delirium subtypes. In the first paper, 23 the actigraph data were represented in graphical summaries showing that delirious patients could be categorized into four groups.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…12 Devices that are capable of measuring the 24-hr motor activity might be helpful in determining more objective criteria for delirium subtypes and the early recognition of delirium. Three different devices are known for the study of motor activity patterns: (1) A discrete accelerometer-based device (such as the ADXL322 from Analod Devices Inc.), [13][14][15][16][17] (2) the activPALÔ (PAL Technologies Ltd., Glasgow, UK), 13,14,17,18 and (3) the Actiwatch Ò (Cambridge Neurotechnology Ltd., Cambridge, UK). [19][20][21][22][23] The discrete accelerometer-based device is similar to the commercial monitor activPAL; it is attached to the thigh and records at a sampling frequency of 50-Hz and at a resolution of 12 bits.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rest-Activity Patterns in Patients with Delirium

Uitert

Jonghe

Gijsel

et al. 2011

Rejuvenation Research

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Section: Results Of Systematic Reviewmentioning

confidence: 99%

Section: Results Of Systematic Reviewmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rest-Activity Patterns in Patients with Delirium

Uitert

Jonghe

Gijsel

et al. 2011

Rejuvenation Research

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show abstract

“…EMG electrodes were attached after the warm-up. Skin was prepared and electrodes were positioned according to SENIAM recommendations [23,24]. Data was gathered while participants performed five repetitions of sitting calf raises and five repetitions of standing calf raises; which isolate the calves muscle group of the lower leg.…”

Section: A Test Protocolmentioning

confidence: 99%

An exploration of eliminating cross-talk in surface electromyography using independent component analysis

Howard

Conway

Harrison

2015

2015 26th Irish Signals and Systems Conference (ISSC)

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Abstract-the purpose of this study was to explore the use of Independent Component Analysis (ICA) on surface Electromyography (EMG) data to distinguish between individual muscle activations due to its capabilities for signal separation. EMG data was gathered on seven participants using the Delsys Trigno Wireless EMG system. Participants performed specific movements which targeted the calves muscle group of the lower leg. EMG sensors were attached according to SENIAM recommendations and extra sensors were attached in non-recommended locations to achieve crosstalk. Signals were acquired using proprietary Delsys software and processed using the ICA algorithm in Matlab to explore crosstalk. Integrated EMG was calculated for all results using custom Matlab code. The results showed moderate levels of agreement between the mixed signals and the original signals (p < 0.01). However, further work is needed to determine the usefulness of the independent components.

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The phenotype of delirium based on a close reading of diagnostic criteria

Oldham,

Weber

2023

Int J Geriat Psychiatry

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ObjectiveAlthough delirium is well known to acute care clinicians, the features required for its diagnosis and how to understand and operationalize them remain sticking points in the field. To clarify the delirium phenotype, we present a close reading of past and current sets of delirium diagnostic criteria.MethodsWe first differentiate the delirium syndrome (i.e., features evaluated at bedside) from additional criteria required for diagnosis. Next, we align related features across diagnostic systems and examine them in context to determine intent. Where criteria are ambiguous, we review common delirium instruments to illustrate how they have been interpreted.ResultsAn acute disturbance in attention is universally attested across diagnostic systems. A second core feature denotes confusion and has been included across systems as disturbance in awareness, impaired consciousness, and thought disorganization. This feature may be better understood as a disturbance in thought clarity and operationalized in terms of neuropsychological domains thereby clearly linking it to global neurocognitive disturbance. Altered level of activity describes a third core feature, including motor and sleep/wake cycle disturbances. Excluding stupor (wherein mental content cannot be assessed due to reduced arousal) from delirium, as in DSM‐5‐TR, is appropriate for a psychiatric diagnosis, but the brain injury exclusion in ICD‐11 is unjustified.ConclusionsThe delirium phenotype involves a disturbance in attention, qualitative thought clarity, and quantitative activity level, including in relation to expected sleep/wake cycles. Future diagnostic systems should include a severity threshold and specify that delirium diagnosis refers to a 24‐h period.

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A classification system for delirium subtyping with the use of a commercial mobility monitor

Cited by 17 publications

References 26 publications

Rest-Activity Patterns in Patients with Delirium

Rest-Activity Patterns in Patients with Delirium

An exploration of eliminating cross-talk in surface electromyography using independent component analysis

The phenotype of delirium based on a close reading of diagnostic criteria

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