Behavioral and psychological symptoms of dementia

Barthel Index © MedChi, 1965. Všechna práva vyhrazena. Držitelem autorských práv na Barthelův index je Maryland State Medical Society. Může se používat zdarma pro nekomerční účely s následující citací: Mahoney FI, Barthel D “Functional evaluation: the Barthel Index.” Maryland State Med Journal 1965;14:56-61. Použito se svolením K úpravě Barthelova indexu nebo k jeho použití pro komerční účely je nutné povolení.

巴氏量表（Barthel Index）知多少？

Background: Alzheimer's disease (AD) and dementia are chronic diseases with progressive deterioration of cognition, function, and behavior leading to severe disability and death. The prevalence of AD and dementia is constantly increasing because of the progressive aging of the population. These conditions represent a considerable challenge to patients, their family and caregivers, and the health system, because of the considerable need for resources allocation. There is no disease modifying intervention for AD and dementia, and the symptomatic pharmacological treatments has limited efficacy and considerable side effects. Non-pharmacological treatment (NPT), which includes a wide range of approaches and techniques, may play a role in the treatment of AD and dementia. Aim: To review, with a narrative approach, current evidence on main NPTs for AD and dementia. Methods: PubMed and the Cochrane database of systematic reviews were searched for studies written in English and published from 2000 to 2018. The bibliography of the main articles was checked to detect other relevant papers. Results: The role of NPT has been largely explored in AD and dementia. The main NPT types, which were reviewed here, include exercise and motor rehabilitation, cognitive rehabilitation, NPT for behavioral and psychological symptoms of dementia, occupational therapy, psychological therapy, complementary and alternative medicine, and new technologies, including information and communication technologies, assistive technology and domotics, virtual reality, gaming, and telemedicine. We also summarized the role of NPT to address caregivers' burden. Conclusions: Although NPT is often applied in the multidisciplinary approach to AD and dementia, supporting evidence for their use is still preliminary. Some studies showed statistically significant effect of NPT on some outcomes, but their clinical significance is uncertain. Well-designed randomized controlled trials with innovative designs are needed to explore the efficacy of NPT in AD and dementia. Further studies are required to offer robust neurobiological grounds for the effect of NPT, and to examine its cost-efficacy profile in patients with dementia.

/pdf/the-multidisciplinary-approach-to-alzheimer-s-disease-and-1lbc4qehhp.pdf

The Multidisciplinary Approach to Alzheimer's Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment

https://hal.archives-ouvertes.fr/hal-01514635/document

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

The concentration in the new era of healthcare is the medical Internet of Things (IoT) according to preventive and prediction ( $p^{2}~Health$ ). In large scale and general perspective, the effective parameters from behavioral, ambient, and physiological domains as the most influencing fields of interest in healthcare must be monitored. In personalized healthcare monitoring, wearables are playing an important role in terms of data measurement and collection. We aim at creating a configurable and adaptable platform for comprehensive parameters monitoring, according to the convenient mode of wearability. Hence, we develop an innovative wrist-worn prototype for ambient monitoring and a flexible IoT gateway. The prototype measures the most critical parameters from an ambient domain. In this platform, via IoT gateway as an intermediate hub between the wearables and the IoT server, bidirectional communication between the end user and medics is established in real time. In addition, the physician as the real-time observer of patients are given the possibility to set up the required parameters for measurement through the IoT gateway and activate/deactivate the sensors on the wearables. Therefore, depending on the target investigation, status of patients, requirements, and demands, medics can determine the setup parameters for measurement. Thus, the application of this platform is not limited to specific groups, but widely may be applied whether in daily routine or medical research investigation. Under the flexible IoT gateway, the new wearables can readily be integrated and synchronized into the platform. The users are not restricted to select only one specific product but there are alternatives as well. To support the solution, experimental results are provided.

A Flexible and Pervasive IoT-Based Healthcare Platform for Physiological and Environmental Parameters Monitoring

Objective:Agitated behaviors are one of the most frequent reasons that patients with dementia are placed in long-term care settings. This study aims to validate the ability of a custom Body Sensor Network (BSN) to capture the presence of agitation against currently accepted subjective measures, the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) and to discriminate between agitation and cognitive decline.Methods:Six patients identified as being at high risk for agitated behaviors were enrolled in this study. The devices were applied at three sites for three hours while behaviors were annotated simultaneously and subsequently repeated twice for each enrolled subject.Results:We found that the BSN was a valid measure of agitation based on construct validity testing and secondary validation using non-parametric ANOVAs.Discussion:The BSN shows promise from these pilot results. Further testing with a larger sample is needed to replicate these results.

https://journals.sagepub.com/doi/pdf/10.1177/1533317512452036

Validation of Noninvasive Body Sensor Network Technology in the Detection of Agitation in Dementia

Caregivers of community-dwelling persons with dementia (PWD) often struggle with challenging and stressful circumstances associated with agitation episodes of the PWD. Such episodes pose a major health risk for both PWD and caregivers. Timely detection can prevent escalation of such events and their hazardous consequences. Wearable sensors are widely used for continuous sensing of physiological parameters, however, reliable inference of behavioral events from such signals is still an open research. Behavior inference in residential settings is challenging due to the prevalence of unpredictable and wide-variety activity patterns. This paper presents a novel methodology to infer the onset of agitation episodes from PWD inertial motion data. As part of a transdisciplinary study, inertial sensors on smart watches are used to unobtrusively capture motion patterns during month-long deployments from eight clinically diagnosed PWD residing in their homes. These patterns are analyzed to build a sequential behavior model using long short-term memory (LSTM) based recurrent neural network. The performance of this model in inferring the onset of agitation episodes is evaluated using data from real deployments. This paper shows the potential of such models in sensing-based behavior inference for real-world applications.

Inferring physical agitation in dementia using smartwatch and sequential behavior models

Agitated behavior is one of the most frequent reasons that patients with dementia are placed in long-term care settings. These behaviors are indicators of distress and are associated with increased risk of injury to the patients and their caregivers. This study aims to explore the ability of a custom inertial wireless body sensor network (BSN) to objectively detect and quantify agitation, validating against currently accepted subjective clinical measures -- the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) -- within the nursing home setting. The ultimate goal is to enable continuous, real-time monitoring of physical agitation in any location over an extended period. Continuous, longitudinal assessment facilitates timely response to agitation events in order to minimize patient distress and risk for injury, to more appropriately titrate pharmacotherapy, and to enable staff (or caregivers) to successfully intervene.Six patients identified as being at high risk for agitated behaviors were enrolled in this pilot study. Patients underwent a series of the above validated tests of memory and agitation. The BSN nodes were applied at three sites on body for three hours while behaviors were annotated simultaneously. This process was subsequently repeated twice for each enrolled subject. The BSN data was then processed using Teager energy analysis, which an earlier study suggested was a promising method for extracting jerky and repetitive movements from inertial data. Results based on construct validity testing for agitation (CMAI) and aggression (ABS) were promising and suggest that additional study with larger sample sizes is warranted.

Continuous, non-invasive assessment of agitation in dementia using inertial body sensors

Agitation in dementia poses a major health risk for both the patients and their caregivers and induces a huge caregiving burden. Early detection of agitation can facilitate timely intervention and prevent escalation of critical episodes. Sensing behavioral patterns for detecting health critical events is a challenging task. Wearable sensors are often employed for sensing physiological signals, but extracting possible biomarkers for confident detection of early agitation is still an open research. In this paper, we employ an ongoing iterative study to explore the motion biomarkers related to agitation in community-dwelling persons with dementia (PWD). This study uses accelerometers in smart watches to capture PWD behavioral patterns unobtrusively. Analysis of the feature space is performed using data from multiple subjects to discriminate among epochs of onset, preset, and offset of agitation while considering inter-person variability in real deployments. This paper shows the prospect of feature space analysis of the motion data for developing early agitation detection models to deploy in the wild.

https://dl.acm.org/doi/pdf/10.1145/3089341.3089344

Motion Biomarkers for Early Detection of Dementia-Related Agitation

Advances in sensing, wireless communication, and data analytics have enabled various monitoring systems for smart health applications. However, many challenges remain to deploy such systems in actual homes, such as achieving robustness, unobtrusiveness, fault tolerance, privacy, and minimal user burden. This paper presents how these challenges were overcome in the realization and successful prototype deployment of the Behavioral and Environmental Sensing and Intervention (BESI) system. BESI is designed to sense behavioral activities using wearables and monitor environmental parameters with in-home sensors. With such data, behavioral patterns can then be modeled to determine associations with environmental attributes and, when appropriate, real-time notifications or interventions can be made based on these models. Challenges in building platforms with residential deployment constraints are discussed. BESI is currently deployed for an in-home study on dementia, and the results are presented to illustrate data collection procedures and system performance.

Azziza Bankole

Papers

Validation of Noninvasive Body Sensor Network Technology in the Detection of Agitation in Dementia

Inferring physical agitation in dementia using smartwatch and sequential behavior models

Continuous, non-invasive assessment of agitation in dementia using inertial body sensors

Motion Biomarkers for Early Detection of Dementia-Related Agitation

BESI: reliable and heterogeneous sensing and intervention for in-home health applications