3 results
P171: Identifying pre-agitation biometric signature in dementia patients: A feasibility study
- Samira Choudhury, Abeer Badawi, Mervin Blair, Sarah Elmi, Khalid Elgazzar, Amer M. Burhan
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- Journal:
- International Psychogeriatrics / Volume 35 / Issue S1 / December 2023
- Published online by Cambridge University Press:
- 02 February 2024, p. 260
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Objectives:
Agitation and aggression (AA) occur frequently in patients with dementia (PwD), are challenging to manage, and are distressing for PwD, families, caregivers, and healthcare systems. Physiological parameters, such as Actigraphy, Heart Rate Variability, and Electrodermal Activity, measured via wearable sensors are correlated with AA in PwD. It is unclear whether these parameters could be compiled into an operational algorithm to create a pre-agitation biometric marker (i.e. parameters of Autonomous Nervous System’s arousal: elevated EDA, more frequent HR, lower heart rate variability (HRV), as well as higher motor activity) capable of predicting episodes of AA. This study will assess the feasibility and clinical utility of collecting physiological parameters via wearable multi-sensor Empatica E4 device in relation to clinically recorded episodes of AA in PwD.
Methods:This study is leveraging a clinical trial (ClinicalTrials.gov/NCT04516057) taking place at Ontario Shores Centre for Mental Health Sciences. Participants are inpatients, males and females, 55-years old or older, with clinically significant AA, and a diagnosis of a Major Neurocognitive Disorder due to Alzheimer’s disease or multiple aetiologies. Participants wear the E4 device for 48 to 72 hours on three occasions during the 8-week study period. Participant demographics, and clinical measures used to assess behavior are collected at specific time intervals during the study period.
Results:The study is ongoing and currently to-date we have been able to acquire approximately 240 hours of recordings from patients. We will be presenting feasibility data (proportion of participants successfully completing a minimum 48-hours of recordings), correlation analysis between physiological measures and clinical measures to identify pre-agitation triggers. Further, we will use generalized linear models to test whether physiological measures can predict pre-agitation triggers. This study will allow estimation of sample size needed to detect a meaningful effect size, which will be determined from the prediction model. Deep learning using Python will be used to create a predictive algorithm using the physiological data to profile participants’ behaviors and detect pre-agitation triggers.
Conclusion:Early detection of AA in PwD will allow caregivers to offer timely, ndividualized, non-medical or medical interventions which will help avoid crises and critical incidents and improve quality of life of the PwD and their caregivers.
S13: Technology enabled care for neuropsychiatric symptoms of dementia: implementation at the point of care
- Amer M. Burhan, Winnie Sun, Mary Chiu, Samira Choudhury, Abeer Badawi, Khalid Elgazzar
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- Journal:
- International Psychogeriatrics / Volume 35 / Issue S1 / December 2023
- Published online by Cambridge University Press:
- 02 February 2024, p. 40
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Symposium Overview
Neuropsychiatric symptoms of dementia (NPSD) are diverse and prevalent group of symptoms that impose significant challenge for people living with dementia (PLWD), their caregivers, and the system of care. Quality standards in all jurisdictions stipulate that individualized, non-pharmacological intervention for NPSD needs to be provided to PLWD before pharmacological interventions are used due to modest effect size and the risks involved in using the latter. Implementation of individualized non-pharmacological plan of care face many challenges including limited staffing, issues with skill development in formal and informal caregivers, difficulty in achieving individualization of behavioral plans with precision, issues with environmental design to name few. To that end, technology has been proposed to address some of these challenges with significant promise at the proof-of-concept level but real-life implementation remains limited.
At the Ontario Shores Centre for Mental Health Sciences in Whitby, Ontario, in collaboration with Ontario Tech University, we have established the “Advancement for Dementia Care Centre”, whereby technological solutions are tested at the point of care considering implementation challenges and engaging formal and informal caregivers in the co-design and implementation of these interventions.
In this symposium, we aim to provide a framework for the successful implementation of different technological solutions for PLWD and NPSD and present the design and preliminary data from four projects that use technology to facilitate standardized, individualized non-pharmacological care for PLWD and their caregivers. The symposium will have 4 talks:
1- Rationale and review of technological solutions to detect emotional distress in PLWD
2- virtual reality to provide reminiscence therapy for PLWD
3- virtual reality to provide caregiver skill development and problem solving
4- the use of simulation platform to provide microcredentialing of health care providers
The objectives of this symposium are:
1- discuss opportunities and challenges related to implementing technological solutions for NPSD at the point of care
2- discuss a framework for co-designing technological solutions with caregivers at the point of care
3- discuss rationale and preliminary findings of 4 projects implemented at the point of care for PLWD presenting with NPSD
This symposium is presented by a multi-ethnic, interprofessional panel including earlier career knowledge mobilization caregiver intervention scientist, a mid career nurse PhD scientist, and a senior clinician investigator geriatric psychiatrist representing a large collaboration team including technology developers, caregivers, engineers, knowledge users and clinicians.
Identifying pre-agitation biometric signature in patients with dementia: A feasibility study
- Samira Choudhury, Abeer Badawi, Khalid Elgazzar, Amer M. Burhan
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- Journal:
- International Psychogeriatrics / Volume 35 / Issue S1 / December 2023
- Published online by Cambridge University Press:
- 02 February 2024, pp. 40-41
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- Article
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- You have access Access
- Export citation
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Background:
Agitation and aggression (AA) occur frequently in patients with dementia (PwD), and cause distress to PwD and caregivers. This study will investigate whether physiological parameters, such as actigraphy, heart rate variability, temperature, and electrodermal activity, measured via wearable sensors, correlate with AA in PwD. It will also explore whether these parameters could be compiled to create a pre-agitation biometric marker capable of predicting episodes of AA in PwD.
Methods:This study will take place at Ontario Shores Centre for Mental Health Sciences. Thirty inpatient participants who are inpatients, males, and females, aged 60 or older, with clinically significant AA, and diagnosis of Major Neurocognitive Disorder will be recruited. Participants will wear the device for 48 to 72 hours on three occasions during an 8-week study period. Participant demographics and clinical measures used to assess behavior will be collected at specific time intervals during the study period.
Ceiling mounted cameras and clinical data are collected to annotate episodes of AA, which will allow identification of peripheral physiological markers “signature” unique to the patient
Results:the algorithm connecting wearable devices, cloud and cameras was tested on healthy volunteers and demonstrated feasibility and reliability. The feasibility of implementation in PwD has been demonstrated in our sample of PwD previously in a sample of 6 participants. Feasibility in this larger sample will be assessed. Correlation analysis between physiological measures, camera capture of agitation onset and clinical measures will be calculated to identify agitation events and pre-agitation triggers. Various machine learning and features extraction/exploration techniques will be used to test whether physiological measures can detect exact time of agitation and predict pre-agitation triggers. This study will provide a reasonable estimation of sample size needed to detect a meaningful effect size, which will be determined from the prediction model.
Conclusion:Early detection of AA in PwD will allow caregivers to offer timely and personalized interventions which will help avoid crises and critical incidents and improve quality of life in PwD and their caregivers.