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Dementia is a progressive brain disease that affects 5.8 million people in the USA. In 2018, more than 16 million Americans provided unpaid care for persons with dementia (PWD). Most of this care is provided by family members and friends yielding 18.5 billion hours of unpaid care worth $233.9 billion.Novel technological approaches to assist caregivers and improve health and function of PWD are being explored. Models to assess and predict behaviors may provide greater quality of life (QOL) for both PWD and their caregivers. The purpose of this paper is to review current available technologies for behavioral interventions in persons with Alzheimer’s disease or other related dementias who live in the home setting. High-quality systematic reviews have studied behavioral or psychological symptoms, some with non-pharmacological interventions (NPI). Some of these involve technology, but others address functional and safety issues. The most frequent behaviors identified in PWD were aggression, agitation, wandering, apathy, and disturbed sleep. There is some evidence of a beneficial effect of NPI on depression and ADL, but no impact on overall behavioral and psychological symptoms of dementia (BPSD), anxiety, or agitation. Technological interventions including combined phone and web impacted caregiver burden and depression. Multimodal sensors to detect agitation show moderate correlation and in-home video recording can improve caregiver’s competence and lessen depression. Acceptance and adherence in use of technologies are essential for PWD and their caregivers in the home setting. There is a need for more RCT’s and individualization of technologies and research must also involve both caregivers and PWD. Systematic reviews addressing behavioral symptoms identify music as the most effective NPI for improving overall BPSD.

Dementia caregiver burden associated with patient agitation is one of the most common reasons for the institutionalization of a person with dementia. We developed an integrative sensing, analytics, modeling, and intervention system that detects early signs of agitation and notifies the caregiver to intervene before escalation.

Synopsis On March 11, 2020, the World Health Organization declared the Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2), a global pandemic. This pandemic disrupted healthcare for patients and providers. Uncertainty about COVID-19 played a significant role in the negative mental health impact in older adults. The effect of increasing age on morbidity and mortality in those who came down with COVID-19 has been substantial. Indeed, the pandemic took a tremendous toll on the mental and physical health of older adults in general with even more severe consequences in more disadvantaged populations within the group.

BACKGROUND Caregiver burden associated with dementia-related agitation is one of the most common reasons for a community-dwelling person living with dementia to transition to a care facility. The Behavioral and Environmental Sensing and Intervention (BESI) for the Dementia Caregiver Empowerment system uses sensing technology, smartwatches, tablets, and data analytics to detect and predict agitation in persons living with dementia and to provide just-in-time notifications and dyad-specific intervention recommendations to caregivers. The BESI system has shown that there is a valid relationship between dementia-related agitation and environmental factors and that caregivers prefer a home-based monitoring system. OBJECTIVE The aim of this study is to obtain input from caregivers of persons living with dementia on the value, usability, and acceptability of the BESI system in the home setting and obtain their insights and recommendations for the next stage of system development. METHODS A descriptive qualitative design with thematic analysis was used to analyze 10 semistructured interviews with caregivers. The interviews comprised 16 questions, with an 80% (128/160) response rate. RESULTS Postdeployment caregiver feedback about the BESI system and the overall experience were generally positive. Caregivers acknowledged the acceptability of the system by noting the ease of use and saw the system as a fit for them. Functionality issues such as timeliness in agitation notification and simplicity in the selection of agitation descriptors on the tablet interface were identified, and caregivers indicated a desire for more word options to describe agitation behaviors. Agitation intervention suggestions were well received by the caregivers, and the resulting decrease in the number and severity of agitation events helped confirm that the BESI system has good value and acceptability. Thematic analysis suggested several subjective experiences and yielded the themes of usefulness and helpfulness. CONCLUSIONS This study determined preferences for assessing caregiver strain and burden, explored caregiver acceptance of the technology system (in-home sensors, actigraph or smart watch technology, and tablet devices), discerned caregiver insights on the burden and stress of caring for persons living with dementia experiencing agitation in dementia, and solicited caregiver input and recommendations for system changes. The themes of usefulness and helpfulness support the use of caregiver knowledge and experience to inform further development of the technology.

Background Caregiver burden associated with dementia-related agitation is one of the most common reasons for a community-dwelling person living with dementia to transition to a care facility. The Behavioral and Environmental Sensing and Intervention (BESI) for the Dementia Caregiver Empowerment system uses sensing technology, smartwatches, tablets, and data analytics to detect and predict agitation in persons living with dementia and to provide just-in-time notifications and dyad-specific intervention recommendations to caregivers. The BESI system has shown that there is a valid relationship between dementia-related agitation and environmental factors and that caregivers prefer a home-based monitoring system. Objective The aim of this study is to obtain input from caregivers of persons living with dementia on the value, usability, and acceptability of the BESI system in the home setting and obtain their insights and recommendations for the next stage of system development. Methods A descriptive qualitative design with thematic analysis was used to analyze 10 semistructured interviews with caregivers. The interviews comprised 16 questions, with an 80% (128/160) response rate. Results Postdeployment caregiver feedback about the BESI system and the overall experience were generally positive. Caregivers acknowledged the acceptability of the system by noting the ease of use and saw the system as a fit for them. Functionality issues such as timeliness in agitation notification and simplicity in the selection of agitation descriptors on the tablet interface were identified, and caregivers indicated a desire for more word options to describe agitation behaviors. Agitation intervention suggestions were well received by the caregivers, and the resulting decrease in the number and severity of agitation events helped confirm that the BESI system has good value and acceptability. Thematic analysis suggested several subjective experiences and yielded the themes of usefulness and helpfulness. Conclusions This study determined preferences for assessing caregiver strain and burden, explored caregiver acceptance of the technology system (in-home sensors, actigraph or smart watch technology, and tablet devices), discerned caregiver insights on the burden and stress of caring for persons living with dementia experiencing agitation in dementia, and solicited caregiver input and recommendations for system changes. The themes of usefulness and helpfulness support the use of caregiver knowledge and experience to inform further development of the technology.

Altered connectivity within neuronal networks is often observed in Alzheimer’s disease. However, delineating pro-cognitive compensatory changes from pathological network decline relies on characterizing network and task effects together. In this study, we interrogated the dynamics of occipito-temporo-frontal brain networks responsible for implicit and explicit memory processes using high-density EEG and dynamic causal modelling. We examined source-localized network activity from patients with Alzheimer’s disease (n = 21) and healthy controls (n = 21), while they performed both visual recognition (explicit memory) and implicit priming tasks. Parametric empirical Bayes analyses identified significant reductions in temporo-frontal connectivity and in subcortical visual input in patients, specifically in the left hemisphere during the recognition task. There was also slowing in frontal left hemisphere signal transmission during the implicit priming task, with significantly more distinct dropout in connectivity during the recognition task, suggesting that these network drop-out effects are affected by task difficulty. Furthermore, during the implicit memory task, increased right frontal activity was correlated with improved task performance in patients only, suggesting that right-hemisphere compensatory mechanisms may be employed to mitigate left-lateralized network dropout in Alzheimer’s disease. Taken together, these findings suggest that Alzheimer’s disease is associated with lateralized memory circuit dropout and potential compensation from the right hemisphere, at least for simpler memory tasks., Tyrer et al. used dynamic causal modelling and parametric empirical Bayes to analyse source-localized network activity in Alzheimer’s disease patients and controls. Patients showed significantly reduced left hemisphere-specific connectivity during explicit memory testing and task-associated increases in right-hemisphere connectivity, suggesting lateralized network dropout and potential right hemisphere compensation in Alzheimer’s disease., Graphical Abstract

The safety and efficacy of electroconvulsive therapy (ECT) for the treatment of psychiatric disorders have been demonstrated in a wide variety of patients, including postoperative patients and those who are pregnant. While several reports highlight the safety of this treatment in heart and liver transplantation patients, there is a relative lack of literature detailing the safety profile of ECT in an individual with recent kidney transplantation. Here, we explore the case of a patient with a recent renal transplant secondary to diabetes-related end-stage renal disease (ESRD) who underwent a successful course of ECT treatment. A 57-year-old Caucasian male with a past psychiatric history of schizoaffective disorder, bipolar type, and a past medical history of end-stage renal disease with recent right renal transplantation was admitted to the inpatient psychiatry unit. The admission was via a temporary detention order (TDO) for suicidality and auditory hallucinations promoting self-harm. The patient’s depressive and delusional history was well-documented and had been refractory to several courses of psychotherapeutic and pharmacologic management. Electroconvulsive therapy was subsequently initiated and was well-tolerated. Treatments progressively alleviated his depressive and psychotic symptoms and did not adversely affect the function of his transplanted kidney, which was closely monitored throughout the treatment process. This case demonstrated the safety and efficacy of ECT treatment in an individual with recent renal transplant and may prompt further trials into establishing safety and efficacy in larger study populations. Published version

Supplemental Material, HFRemoteEthnographic for BESI: Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment—Phases 1 and 2 by Azziza Bankole, Martha S. Anderson, Nutta Homdee, Ridwan Alam, Ashley Lofton, Nykesha Fyffe, Hilda Goins, Temple Newbold, Tonya Smith-Jackson and John Lach in American Journal of Alzheimer"s Disease & Other Dementias

Agitation in persons with dementia (PWD) poses major health risks both for themselves and for their caregivers. Passive sensing based continuous behavior tracking can prevent the escalation of such episodes. But, predicting such behavior from sensor streams, especially in real-world residential settings, is still an active area of research. Major challenges include the sparsity, unpredictability, and variations in such behavior, as well as the "weak" annotations from real-world participants. This paper proposes a novel approach to overcome these issues in predicting agitation episodes from the PWD's wrist motion data. In a transdisciplinary study on dementia dyads residing in their homes, the PWD motion is continuously sensed from their smart watch inertial sensors, while agitation episodes are actively marked by the caregivers. The data from 10 residential deployments, each with 30 days duration, are analyzed in this paper, and multiple-instance learning (MIL) based models are implemented to learn from such sparse and weakly annotated data. These models are compared with single-instance models in predicting the agitated behavior. The results show the potential of MIL models in sparsely labeled behavior inference from wearables in-the-wild.

Background and Objectives: Caregiver burden associated with dementia-related agitation is one of the commonest reasons a community-dwelling person with dementia (PWD) transitions to a care facility. Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment (BESI) is a system of body-worn and in-home sensors developed to provide continuous, noninvasive agitation assessment and environmental context monitoring to detect early signs of agitation and its environmental triggers. Research Design and Methods: This mixed methods, remote ethnographic study is explored in a 3-phase, multiyear plan. In Phase 1, we developed and refined the BESI system and completed usability studies. Validation of the system and the development of dyad-specific models of the relationship between agitation and the environment occurred in Phase 2. Results: Phases 1 and 2 results facilitated targeted changes in BESI, thus improving its overall usability for the final phase of the study, when real-time notifications and interventions will be implemented. Conclusion: Our results show a valid relationship between the presence of dementia related agitation and environmental factors and that persons with dementia and their caregivers prefer a home-based monitoring system like BESI.

Aging is now an integral part of society, and in short, we expect to grow old. The average lifespan has increased as a result of declining childhood and elderly mortality rates. Associated with our increased lifespan is the rising prevalence of chronic illnesses such as cardiovascular diseases, neurocognitive disorders, diabetes, cancer, and mobility impairment. Health-care providers must take into consideration the impact of the various factors that have an impact on older adults, how they experience illnesses, and their ability to communicate their experience to us. The setting in which older adults are seen and treated necessarily will have an impact on their experience and expectations. Understanding these dynamics is a critical part in the caregiving process. In this chapter, we provide a background on the different settings that older adults receive treatment and explore their role in presentation, diagnosis, and treatment of various psychiatric disorders.

Introduction Caregiver burden associated with dementia-related agitation is one of the most common reasons community-dwelling persons with dementia (PWD) transition to a care facility. Agitation in dementia can be defined as “inappropriate verbal, vocal, or motor activity that is not explained by needs or confusion per se” and is “consistent with emotional distress”. Agitation can be physically non-aggressive, aggressive, or verbally agitated behavior. Up to 90% of PWDs experience agitation and it is one of the principal factors for institutionalization. Agitation can be unpredictable, remain undetected in the early stages, and quickly escalate. Caregivers of PWD report high levels of emotional, physical, and financial stress and are susceptible to disease and health complications. BESI is an interactive, cybersociophysical system comprising of in-home and body-worn sensors, a caregiver tablet interface, online modeling, and caregiver notifications built to help detect the environmental triggers and early signs of agitation in PWD. Methods This is a mixed method, prospective study using descriptive, qualitative, and quantitative measures of caregiver-PWD dyads. Baseline psychosocial and behavioral status for both members of the dyad were assessed using validated assessment tools. Phase 1 (System Verification) - controlled settings (laboratory and homes of 2 healthy volunteers) and study settings (homes of 2 study dyads) & usability testing Phase 2 (System Effectiveness) - homes of 10 study dyads for 30 days each Home Visits First: provide detailed description of study, obtain informed consent, and document demographic and medical information. Second: complete assessment battery and BESI system installation. Results Phase 1 The tablet application usability was assessed and revised showing it to be a viable tool. We observed and documented caregiver emotional-state evolution over the day in relation to agitation episodes, the correlation between agitation events and accelerometer Teager energy magnitudes, and the variability in environmental parameters across rooms temporally and during agitation episodes. Audio levels did not yield enough information for more complex audio processing such as verbal agitation detection. We concluded that additional model training and evaluation was needed for physical agitation detection and PWD room localization. Phase 2 An upgraded audio data collection system was implemented to provide more complex audio processing. Comparing this with the verbal agitation episode reports on the tablet application, this method produced a 76.7% accuracy in detecting verbal agitation events. Multiple models were explored for physical agitation detection, with long short-term memory (LSTM) cell based recurrent neural network providing the highest performance. Decision rules was used to structure covariance factor retention for each dyad to feed into artificial neural network models. Doorway-based sensors and Markov models with room transition probabilities provided ∼90% room-level tracking accuracy of the PWD. Conclusions Phases 1 & 2 results facilitated targeted changes in BESI, thus improving its overall usability for the final phase of the study. This research was funded by This study is funded by the National Science Foundation Smart and Connected Health Program. [Grant Number 1418622]

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.

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.

The patient is a 40-year-old white man who was admitted to our inpatient service involuntarily on a court order for acute psychosis. He had been expressing bizarre delusions about “the devil” and winning the lottery. He expressed paranoid delusions about people being after him for his money. He also experienced auditory and visual hallucinations. He did not have any disorganized thoughts or behavior outside of this delusional system and did not show any negative symptoms of psychosis. He did not have any suicidal or homicidal thoughts and was involuntarily committed to treatment based on his inability to take care of himself. He did not have manic or depressive symptoms. He reported that he had stopped drinking alcohol about a week before the index admission. He denied experiencing any withdrawal symptoms, such as tremors, diapho

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.

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.

Introduction: Many problems prevalent in the geriatric population, such as dementia and depression, have traditionally been treated in the primary care setting, only being referred to a geriatric specialist when it is considered to be more advanced. While this does have benefits and improves access to healthcare, it presents challenges of its own and there has been a push to improve coordination between mental health and primary care. It has been noted that depression and early stage of dementia are often undiagnosed or inadequately treated by non-psychiatric or non-geriatric specialties. Primary care physicians may not be as likely to prescribe the full range of available or approved medications, or may not be comfortable with higher doses of psychiatric medications for example. There may also be a delay in appropriate referral of these patients to a specialist that could affect the outcome for these patients on the long run. Therefore, not only medical professions, but also patients call for integration of mental health and primary care to reduce the fragmented and inadequate care of mental health problems in primary care patients. Methods: We would like to use the electronic medical records from Carilion Roanoke Memorial Hospital, one of the largest hospitals in Virginia, to gather data on patients with such illnesses who are being followed by primary care, geriatric psychiatry, and geriatric medicine and to compare the pharmacological interventions. Statistical analysis including Statistical Distributions, ANOVA, T tests, and Chi-squared test are applied to analyze the patient’s diagnosis, treatments and the prognosis of about eight thousands geriatric patients in the database, in which, the patients are classified according to gender and age groups including 60-70 years old, 70 to 80 years old and over 80 years old. The differences in diagnosis interventions, treatment plan, psychotropic medication applications and dosing, as well as the long-term prognosis of depression and dementia in the geriatric population in the Roanoke Valley area are compared. Results: The preliminary data shows that benzodiazepines, particularly clonazepam and alprazolam, are the most often prescribed sedatives by primary care in treating depression or dementia with behavior disturbance. These medications have significant adverse effects, such patient falls and worsening dementia, therefore, significantly increase morbidity in geriatric patients. Preliminary data also shows that it is common for drugs to be prescribed under the effective dose for the illness. In addition, because of the lack of experience in diagnosis illness using the latest criteria and scales, patients in primary care then to have high rates of under diagnosis of dementia and depression. Conclusions: With our findings, we hope to emphasize the importance to improve care integration between psychiatry and primary care so that patients will be treated with appropriate medications at the appropriate time, as well as have appropriate and timely referrals to geriatric psychiatry when needed.