Detecting agitation and aggression in people with dementia using sensors-A systematic review

Use of actigraphy for monitoring agitation and rest-activity cycles in patients with acute traumatic brain injury in the ICU

BACKGROUND

In traumatic brain injury patients (TBI) admitted to the intensive care unit (ICU), agitation can lead to accidental removal of catheters, devices as well as self-extubation and falls. Actigraphy could be a potential tool to continuously monitor agitation. The objectives of this study were to assess the feasibility of monitoring agitation with actigraphs and to compare activity levels in agitated and non-agitated critically ill TBI patients.

METHODS

Actigraphs were placed on patients' wrists; 24-hour monitoring was continued until ICU discharge or limitation of therapeutic efforts. Feasibility was assessed by actigraphy recording duration and missing activity count per day.

RESULTS

Data from 25 patients were analyzed. The mean number of completed day of actigraphy per patient was 6.5 ± 5.1. The mean missing activity count was 20.3 minutes (±81.7) per day. The mean level of activity measured by raw actigraphy counts per minute over 24 hours was higher in participants with agitation than without agitation.

CONCLUSIONS

This study supports the feasibility of actigraphy use in TBI patients in the ICU. In the acute phase of TBI, agitated patients have higher levels of activity, confirming the potential of actigraphy to monitor agitation.

Detecting agitation and aggression in persons living with dementia: a systematic review of diagnostic accuracy

OBJECTIVE

40-60% of persons living with dementia (PLWD) experience agitation and/or aggression symptoms. There is a need to understand the best method to detect agitation and/or aggression in PLWD. We aimed to identify agitation and/or aggression tools that are validated against a reference standard within the context of PLWD.

METHODS

Our study was registered on PROSPERO (CRD42020156708). We searched MEDLINE, Embase, and PsycINFO up to April 22, 2024. There were no language or date restrictions. Studies were included if they used any tools or questionnaires for detecting either agitation or aggression compared to a reference standard among PLWD, or any studies that compared two or more agitation and/or aggression tools in the population. All screening and data extraction were done in duplicates. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Data extraction was completed in duplicates by two independent authors. We extracted demographic information, prevalence of agitation and/or aggression, and diagnostic accuracy measures. We also reported studies comparing the correlation between two or more agitation and/or aggression tools.

RESULTS

6961 articles were screened across databases. Six articles reporting diagnostic accuracy measures compared to a reference standard and 30 articles reporting correlation measurements between tools were included. The agitation domain of the Spanish NPI demonstrated the highest sensitivity (100%) against the agitation subsection of the Spanish CAMDEX. Single-study evidence was found for the diagnostic accuracy of commonly used agitation scales (BEHAVE-AD, NPI and CMAI).

CONCLUSIONS

The agitation domain of the Spanish NPI, the NBRS, and the PAS demonstrated high sensitivities, and may be reasonable for clinical implementation. However, a limitation to this finding is that despite an extensive search, few studies with diagnostic accuracy measurements were identified. Ultimately, more research is needed to understand the diagnostic accuracy of agitation and/or aggression detection tools among PLWD.

Evaluation of the accuracy of a UWB tracker for in-home positioning for older adults

The population of older adults is rapidly growing. In-home monitoring systems have been used to support aging-in-place. Ambient sensors or wearable localizers can be used but may be too low resolution, while camera systems are invasive to privacy. Ultra-wideband (UWB) localization offers precise positioning by placing anchors throughout the house and wearing a tag that is tracked by the anchors. In this study, the accuracy of UWB for indoor tracking was evaluated in a motion capture gait lab and in a mock condo in the Glenrose Rehabilitation Hospital. First, the configuration of UWB was tested, changing factors related to sampling time, anchor placement and line-of-sight. Comparing these factors to the configurations recommended by the manufacturer guidelines, accuracies remained within 14 cm. We then performed static and dynamic accuracy tests, with dynamic testing comprised of rolling and walking motions. In the motion capture lab, we found localization accuracies of 7.0 ± 11.1 cm while in the mock condo, we found accuracies of 27.3 ± 12.9 cm. Dynamic testing with rolling motions had an average of 19.1 ± 1.6 cm while walking was 20.5 ± 4.2 cm. The mean accuracy of UWB is within the 30 cm target for indoor localization.

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review

BACKGROUND

Discomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort.

METHODS

A systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted.

RESULTS

Of the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase.

CONCLUSIONS

Noninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.

Long-term Care Facility Staff's Experience of Safety Activities: A Qualitative Study

BACKGROUND

Ensuring the safety and quality of care is paramount in long-term care facilities due to residents' vulnerability.

OBJECTIVE

We explored the experiences of long-term care facility staff (eg, registered nurses [RNs], certified nursing assistants [CNAs], care workers, social workers, and physical therapists) in safety activities, aiming to understand their meaning and nature.

METHODS

We conducted qualitative focus group interviews with 25 participants, specifically addressing safety issues in long-term care facilities through the use of open-ended questions. We transcribed the data and conducted thematic analysis.

RESULTS

Participants engaged in discussions about various challenges, including assisting residents in maintaining physical comfort, managing behavioral and psychological symptoms of dementia, ensuring medication safety, implementing infection control practices, and providing adequate training on fire prevention, evacuation, and response protocols. Themes identified were "physical comfort," "managing dementia symptoms," "drug administration," "infection control," and "fire prevention."

CONCLUSIONS

The staff emphasized the safety of residents as their highest priority. Considering the lack of registered nurses in long-term care facilities, ongoing training and supervision are necessary to ensure that other long-term care facility staff can perform safety activities.

Walking path images from real-time location data predict degree of cognitive impairment

BACKGROUND

We propose a novel approach that uses spatial walking patterns produced by real-time location systems to classify the severity of cognitive impairment (CI) among residents of a memory care unit.

METHODS

Each participant was classified as "No-CI", "Mild-Moderate CI" or "Severe CI" based on their Mini-Mental State Examination scores. The location data was distributed into windows of various durations (5, 10, 15 and 30 min) and transformed into images used to train a custom convolutional neural network (CNN) at each window size. Class Activation Mapping was applied to the top-performing models to determine the features of images associated with each class.

RESULTS

The best performing model achieved an accuracy of 87.38 % (30-min window length) with an overall pattern that larger window sizes perform better. The class activation maps were effectively consolidated into a Cognitive Impairment Classification Value (CICV) score that distinguishes between No-CI, Mild-Moderate CI, and Severe CI.

CONCLUSION

The class activation maps show that the CNN made relevant and intuitive distinctions for paths corresponding to each class. Future work should validate the proposed techniques with participants who are well-characterized clinically, over larger and diversified settings, and towards classification of neuropsychiatric symptoms such as motor agitation, mood, or apathy.

The Predictors of Negative and Positive Affect among People with Dementia: A Cross-Sectional Study

Background and Objectives: This cross-sectional study examined the predictors of negative and positive affect among individuals with dementia. Materials and Methods: A sample of 102 Jordanian participants diagnosed with dementia was recruited from residential care facilities, and data were collected using different measures. Results: The results revealed that higher levels of negative affect were significantly associated with increased physical and verbal agitation among individuals with dementia. Conversely, lower levels of positive affect were associated with residing in a nursing home. Conclusions: These findings highlight the importance of recognizing the impact of both negative and positive affect on the well-being of individuals with dementia. Interventions targeting the reduction of negative affect and promoting positive affect could alleviate agitation and enhance emotional closeness in this population.

The Significance and Limitations of Sensor-based Agitation Detection in People Living with Dementia

Agitation, a commonly observed behaviour in people living with dementia (PLwD), is frequently interpreted as a response to physiological, environmental, or emotional stress. Agitation has the potential to pose health risks to both individuals and their caregivers, and can contribute to increased caregiver burden and stress. Early detection of agitation can facilitate with timely intervention, which has the potential to prevent escalation to other challenging behaviors. Wearable and ambient sensors are frequently used to monitor physiological and behavioral conditions and the collected signals can be engaged to detect the onset of an agitation episode. This paper delves into the current sensor-based methods for detecting agitation in PLwD, and reviews the strengths and limitations of existing works. Future directions to enable real-time agitation detection to empower caregivers are also deliberated, with a focus on their potential to reduce caregiver burden by facilitating early support, assistance and interventions to timely manage agitation episodes in PLwD.

The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study

BACKGROUND

Neuropsychiatric symptoms (NPS) are prevalent in the early clinical stages of Alzheimer's disease (AD) according to proxy-based instruments. Little is known about which NPS clinicians report and whether their judgment aligns with proxy-based instruments. We used natural language processing (NLP) to classify NPS in electronic health records (EHRs) to estimate the reporting of NPS in symptomatic AD at the memory clinic according to clinicians. Next, we compared NPS as reported in EHRs and NPS reported by caregivers on the Neuropsychiatric Inventory (NPI).

METHODS

Two academic memory clinic cohorts were used: the Amsterdam UMC (n = 3001) and the Erasmus MC (n = 646). Patients included in these cohorts had MCI, AD dementia, or mixed AD/VaD dementia. Ten trained clinicians annotated 13 types of NPS in a randomly selected training set of n = 500 EHRs from the Amsterdam UMC cohort and in a test set of n = 250 EHRs from the Erasmus MC cohort. For each NPS, a generalized linear classifier was trained and internally and externally validated. Prevalence estimates of NPS were adjusted for the imperfect sensitivity and specificity of each classifier. Intra-individual comparison of the NPS classified in EHRs and NPS reported on the NPI were conducted in a subsample (59%).

RESULTS

Internal validation performance of the classifiers was excellent (AUC range: 0.81-0.91), but external validation performance decreased (AUC range: 0.51-0.93). NPS were prevalent in EHRs from the Amsterdam UMC, especially apathy (adjusted prevalence = 69.4%), anxiety (adjusted prevalence = 53.7%), aberrant motor behavior (adjusted prevalence = 47.5%), irritability (adjusted prevalence = 42.6%), and depression (adjusted prevalence = 38.5%). The ranking of NPS was similar for EHRs from the Erasmus MC, although not all classifiers obtained valid prevalence estimates due to low specificity. In both cohorts, there was minimal agreement between NPS classified in the EHRs and NPS reported on the NPI (all kappa coefficients < 0.28), with substantially more reports of NPS in EHRs than on NPI assessments.

CONCLUSIONS

NLP classifiers performed well in detecting a wide range of NPS in EHRs of patients with symptomatic AD visiting the memory clinic and showed that clinicians frequently reported NPS in these EHRs. Clinicians generally reported more NPS in EHRs than caregivers reported on the NPI.

Privacy-protecting behaviours of risk detection in people with dementia using videos

BACKGROUND

People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns.

PURPOSE

In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia.

METHODS

We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies.

RESULTS

We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach.

CONCLUSIONS

This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community.

Evidence and User Considerations of Home Health Monitoring for Older Adults: Scoping Review

BACKGROUND

Home health monitoring shows promise in improving health outcomes; however, navigating the literature remains challenging given the breadth of evidence. There is a need to summarize the effectiveness of monitoring across health domains and identify gaps in the literature. In addition, ethical and user-centered frameworks are important to maximize the acceptability of health monitoring technologies.

OBJECTIVE

This review aimed to summarize the clinical evidence on home-based health monitoring through a scoping review and outline ethical and user concerns and discuss the challenges of the current user-oriented conceptual frameworks.

METHODS

A total of 2 literature reviews were conducted. We conducted a scoping review of systematic reviews in Scopus, MEDLINE, Embase, and CINAHL in July 2021. We included reviews examining the effectiveness of home-based health monitoring in older adults. The exclusion criteria included reviews with no clinical outcomes and lack of monitoring interventions (mobile health, telephone, video interventions, virtual reality, and robots). We conducted a quality assessment using the Assessment of Multiple Systematic Reviews (AMSTAR-2). We organized the outcomes by disease and summarized the type of outcomes as positive, inconclusive, or negative. Second, we conducted a literature review including both systematic reviews and original articles to identify ethical concerns and user-centered frameworks for smart home technology. The search was halted after saturation of the basic themes presented.

RESULTS

The scoping review found 822 systematic reviews, of which 94 (11%) were included and of those, 23 (24%) were of medium or high quality. Of these 23 studies, monitoring for heart failure or chronic obstructive pulmonary disease reduced exacerbations (4/7, 57%) and hospitalizations (5/6, 83%); improved hemoglobin A1c (1/2, 50%); improved safety for older adults at home and detected changing cognitive status (2/3, 66%) reviews; and improved physical activity, motor control in stroke, and pain in arthritis in (3/3, 100%) rehabilitation studies. The second literature review on ethics and user-centered frameworks found 19 papers focused on ethical concerns, with privacy (12/19, 63%), autonomy (12/19, 63%), and control (10/19, 53%) being the most common. An additional 7 user-centered frameworks were studied.

CONCLUSIONS

Home health monitoring can improve health outcomes in heart failure, chronic obstructive pulmonary disease, and diabetes and increase physical activity, although review quality and consistency were limited. Long-term generalized monitoring has the least amount of evidence and requires further study. The concept of trade-offs between technology usefulness and acceptability is critical to consider, as older adults have a hierarchy of concerns. Implementing user-oriented frameworks can allow long-term and larger studies to be conducted to improve the evidence base for monitoring and increase the receptiveness of clinicians, policy makers, and end users.

Individual differences in the temporal relationship between sleep and agitation: a single-subject study in nursing home residents with dementia experiencing sleep disturbance and agitation

OBJECTIVES

Previous studies on the interrelationship between sleep and agitation relied on group-aggregates and so results may not be applicable to individuals. This proof-of-concept study presents the single-subject study design with time series analysis as a method to evaluate the association between sleep and agitation in individual nursing home residents using actigraphy.

METHOD

To record activity, three women and two men (aged 78-89 years) wore the MotionWatch 8© (MW8) for 9 consecutive weeks. Total sleep time and agitation were derived from the MW8 data. We performed time series analysis for each individual separately. To gain insight into the experiences with the actigraphy measurements, care staff filled out an investigator-developed questionnaire on their and participants' MW8 experiences.

RESULTS

A statistically significant temporal association between sleep and agitation was present in three out of five participants. More agitation was followed by more sleep for participant 1, and by less sleep for participant 4. As for participants 3 and 4, more sleep was followed by more agitation. Two-thirds of the care staff members (16/24) were positive about the use of the MW8. Acceptability of the MW8 was mixed: two residents refused to wear the MW8 thus did not participate, one participant initially experienced the MW8 as somewhat unpleasant, while four participants seemed to experience no substantial problems.

CONCLUSION

A single-subject approach with time series analysis can be a valuable tool to gain insight into the temporal relationship between sleep and agitation in individual nursing home residents with dementia experiencing sleep disturbance and agitation.

CNN for Elderly Wandering Prediction in Indoor Scenarios

This work proposes a way to detect the wandering movement of Alzheimer’s patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.

Applications and Outcomes of Internet of Things for Patients with Alzheimer’s Disease/Dementia: A Scoping Review

Objectives

We aimed to identify and classify the Internet of Things (IoT) technologies used for Alzheimer's disease (AD)/dementia as well as the healthcare aspects addressed by these technologies and the outcomes of the IoT interventions. Methodology. We searched PubMed/MEDLINE, IEEE Explore, Web of Science, OVID, Scopus, Embase, Cochrane, and Google Scholar. In total, 13,005 papers were reviewed, 36 of which were finally selected. All the reviews were independently carried out by two researchers. In the case of any disagreement, the problem was resolved by holding a meeting and exchanging views. Due to the diversity of the reviewed studies, narrative analysis was performed.

Results

Among the technologies used for the patients including radio frequency identification (RFID), near field communication (NFC), ZigBee, Bluetooth, global positioning system (GPS), sensors, and cameras, the sensors were employed in 36 studies, most of which were switch and vital sign monitoring sensors. The most common aspects of AD/dementia care monitored using these technologies were activities of daily living (ADLs) in 27 studies, followed by sleep patterns and disease diagnosis in 19 and 14 studies, respectively. Sleeping, medication, vital signs, agitation, memory, social interaction, apathy, movement, tracking, and fall were other aspects monitored by IoT. Then, their outcomes were reported.

Conclusion

Using IoT for AD/dementia provides many opportunities for considering various aspects of this disease. Moreover, the ability to use various technologies for gathering patient-related data provides a comprehensive application for almost all aspects of the patients' care with high accuracy.

Prevalence and correlates of aggressive behavior in psychiatric inpatient populations

Aggressive behavior in patients with psychiatric disorders is attracting increasing research interest. One reason for this is that psychiatric patients are generally considered more likely to be aggressive, which raises a related question of whether diagnoses of psychiatric disorders predict the prevalence of aggressive behavior. Predicting aggression in psychiatric wards is crucial, because aggressive behavior not only endangers the safety of both patients and staff, but it also extends the hospitalization times. Predictions of aggressive behavior also need careful attention to ensure effective treatment planning. This literature review explores the relationship between aggressive behavior and psychiatric disorders and syndromes (dementia, psychoactive substance use, acute psychotic disorder, schizophrenia, bipolar affective disorder, major depressive disorder, obsessive-compulsive disorder, personality disorders and intellectual disability). The prevalence of aggressive behavior and its underlying risk factors, such as sex, age, comorbid psychiatric disorders, socioeconomic status, and history of aggressive behavior are discussed as these are the components that mostly contribute to the increased risk of aggressive behavior. Measurement tools commonly used to predict and detect aggressive behavior and to differentiate between different forms of aggressive behavior in both research and clinical practice are also reviewed. Successful aggression prevention programs can be developed based on the current findings of the correlates of aggressive behavior in psychiatric patients.

Conceptualising fairness: three pillars for medical algorithms and health equity

OBJECTIVES

Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature.

METHODS

We conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo).

RESULTS

Our synthesis identified 'Three Pillars for Fairness': transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare.

DISCUSSION

We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients.

CONCLUSION

We conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.

Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review

Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.

Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models

Introduction

Behavioral and psychological symptoms of dementia (BPSD) signal distress or unmet needs and present a risk to people with dementia and their caregivers. Variability in the expression of these symptoms is a barrier to the performance of digital biomarkers. The aim of this study was to use wearable multimodal sensors to develop personalized machine learning models capable of detecting individual patterns of BPSD.

Methods

Older adults with dementia and BPSD (n = 17) on a dementia care unit wore a wristband during waking hours for up to 8 weeks. The wristband captured motion (accelerometer) and physiological indicators (blood volume pulse, electrodermal activity, and skin temperature). Agitation or aggression events were tracked, and research staff reviewed videos to precisely annotate the sensor data. Personalized machine learning models were developed using 1‐minute intervals and classifying the presence of behavioral symptoms, and behavioral symptoms by type (motor agitation, verbal aggression, or physical aggression).

Results

Behavioral events were rare, representing 3.4% of the total data. Personalized models classified behavioral symptoms with a median area under the receiver operating curve (AUC) of 0.87 (range 0.64–0.95). The relative importance of the different sensor features to the predictive models varied both by individual and behavior type.

Discussion

Patterns of sensor data associated with BPSD are highly individualized, and future studies of the digital phenotyping of these behaviors would benefit from personalization.

Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review

INTRODUCTION

There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting.

OBJECTIVE

This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings.

METHODS

Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes.

RESULTS

A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident's health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making.

CONCLUSION

This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Background

Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.

Objective

This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.

Methods

Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.

Results

The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.

Conclusions

The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

Access to and interest in assistive technology for home-dwelling people with dementia during the COVID-19 pandemic (PAN.DEM)

The COVID-19 restrictions affect daily living in Norway, including home-dwelling people with dementia, and researchers conducting clinical trials in dementia care. In this paper, we 1) describe the development of a pandemic cohort (PAN.DEM) incorporated in the LIVE@Home.Path, an ongoing clinical intervention trial on resource utilisation including home-dwelling people with dementia and their caregivers (N = 438 dyads), 2) describe pre-pandemic use of assistive technology and 3) explore the extent to which COVID-19 restrictions increase caregivers interest in innovation in the PAN.DEM cohort (N = 126). Our main finding is that assistive technology is available to 71% pre-pandemic; the vast majority utilise traditional stove guards and safety alarms, only a few operate sensor technology, including GPS, fall detectors or communication aids. In response to COVID-19, 17% show increased interest in technology; being less familiar with operating a telephone and having higher cognitive functioning are both associated with increased interest. We conclude that wearable and sensor technology has not yet been fully implemented among people with dementia in Norway, and few caregivers show increased interest under the restrictions. Clinicaltrials.gov (NCT0404336).

Towards Responsible Implementation of Monitoring Technologies in Institutional Care

Increasing awareness of errors and harms in institutional care settings, combined with rapid advancements in artificial intelligence, have resulted in a widespread push for implementing monitoring technologies in institutional settings. There has been limited critical reflection in gerontology regarding the ethical, social, and policy implications of using these technologies. We critically review current scholarship regarding use of monitoring technology in institutional care, and identify key gaps in knowledge and important avenues for future research and development.

Wearables bei Demenzerkrankungen

Demenzerkrankungen führen durch den schleichenden Abbau kognitiver, sozialer und emotionaler Fähigkeiten, auch zu einem Verlust von Autonomie und Selbstbestimmtheit. Wearables sind am Körper getragene Sensoren: Akzelerometer und GPS-Tracker sind im Freizeit- und Fitnessbereich allgegenwärtig – sie zeichnen Bewegungs- und Positionsdaten auf. Das Potenzial, diese bei Demenzpatienten einzusetzen ist groß und wird intensiv beforscht. Wearables sind tlw. auch am Markt erhältlich (bspw. GPS-Tracker in Schuhsohlen). Informationen über Gangbild und Bewegungsdaten können auch Hinweise auf das Sturzrisiko, Verhaltensstörungen/Life-Events oder differenzialdiagnostische Aspekte geben. Trotz des großen Potenzials dürfen ethische Aspekte betreffend die Privatsphäre und den Datenschutz in der Entwicklung nicht außer Acht gelassen werden. Dieser Artikel gibt einen Überblick über die aktuelle Entwicklung von Wearables und damit verbundene ethische Aspekte.

The Frontal Behavioral Battery: A Measure of Frontal Lobe Symptoms in Brain Aging and Neurodegenerative Disease

BACKGROUND

Approximately 90%of persons living with dementia experience behavioral symptoms, including frontal lobe features involving motivation, planning, social behavior, language, personality, mood, swallowing, and gait.

OBJECTIVE

We conducted a two-stage study with a development sample (n = 586) and validation sample (n = 274) to evaluate a brief informant-rated measure of non-cognitive features of frontal lobe dysfunction: the Frontal Behavioral Battery (FBB).

METHODS

In the development sample, internal consistency, principal factor analysis, and correlations between the FBB and outcomes were evaluated. In the validation sample, we examined (a) FBB scores by diagnosis, (b) known-group validity by demographics, subjective complaints, and dementia staging, and (c) correlation between FBB and MRI volumes. Receiver operator characteristic curves assessed the ability of the FBB to discriminate individuals with frontal lobe features due to a neurodegenerative disease.

RESULTS

The FBB characterized 11 distinct frontal lobe features. Individuals with dementia with Lewy bodies and frontotemporal degeneration had the greatest number of frontal lobe features. Premorbid personality traits of extroversion, agreeableness, and openness were associated with fewer frontal lobe behavioral symptoms, while subjective cognitive complaints were associated with greater symptoms. The FBB provided very good discrimination between individuals with and without cognitive impairment (diagnostic odds ratio: 13.1) and between individuals with and without prominent frontal lobe symptoms (diagnostic odds ratio: 84.8).

CONCLUSION

The FBB may serve as an effective and efficient method to assess the presence of non-cognitive symptoms associated with frontal lobe dysfunction, but in a brief fashion that could facilitate its use in clinical care and research.

Nonparametric time series summary statistics for high-frequency accelerometry data from individuals with advanced dementia

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Modern accelerometers can record continuous observations on a single individual for several days at a sampling frequency of the order of one hertz. Such rich and lengthy data sets provide new opportunities for statistical insight, but also pose challenges in selecting from a wide range of possible summary statistics, and how the calculation of such statistics should be optimally tuned and implemented. In this paper, we build on existing approaches, as well as propose new summary statistics, and detail how these should be implemented with high frequency accelerometry data. We test and validate our methods on an observed data set from 26 recordings from individuals with advanced dementia and 14 recordings from individuals without dementia. We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation. We perform a detailed analysis indicating how the time series should be optimally subsampled to calculate IV, and recommend a subsampling rate of approximately 5 minutes for the dataset that has been studied. In addition, we propose the use of the DFA scaling exponent separately for daytime and nighttime, to further separate effects between individuals. We compare the relationships between all these methods and show that they effectively capture different features of the time series.

Sensing a problem: Proof of concept for characterizing and predicting agitation

INTRODUCTION

Agitation, experienced by patients with dementia, is difficult to manage and stressful for caregivers. Currently, agitation is primarily assessed by caregivers or clinicians based on self-report or very brief periods of observation. This limits availability of comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of this article is to provide proof of concept for characterizing and predicting agitation using a system that continuously monitors patients' activities and living environment within memory care facilities.

METHODS

Continuous and unobtrusive monitoring of a participant is achieved using behavioral sensors, which include passive infrared motion sensors, door contact sensors, a wearable actigraphy device, and a bed pressure mat sensor installed in the living quarters of the participant. Environmental sensors are also used to continuously assess temperature, light, sound, and humidity. Episodes of agitation are reported by nursing staff. Data collected for 138 days were divided by 8-hour nursing shifts. Features from agitated shifts were compared to those from non-agitated shifts using t-tests.

RESULTS

A total of 37 episodes of agitation were reported for a male participant, aged 64 with Alzheimer's disease, living in a memory care unit. Participant activity metrics (eg, transitions within the living room, sleep scores from the bedmat, and total activity counts from the actigraph) significantly correlated with occurrences of agitation at night (P < 0.05). Environmental variables (eg, humidity) also correlated with the occurrences of agitation at night (P < 0.05). Higher activity levels were also observed in the evenings before agitated nights.

DISCUSSION

A platform of sensors used for unobtrusive and continuous monitoring of participants with dementia and their living space seems feasible and shows promise for characterization of episodes of agitation and identification of behavioral and environmental precipitants of agitation.

Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study

OBJECTIVES

Alzheimer's Disease (AD)-related behavioral symptoms (i.e. agitation and/or pacing) develop in nearly 90% of AD patients. In this N = 1 study, we provide proof-of-concept of detecting changes in movement patterns that may reflect underlying behavioral symptoms using a highly novel radio sensor and identifying environmental triggers.

METHODS

The Emerald device is a Wi-Fi-like box without on-body sensors, which emits and processes radio-waves to infer patient movement, spatial location and activity. It was installed for 70 days in the room of patient 'E', exhibiting agitated behaviors.

RESULTS

Daily motion episode aggregation revealed motor activity fluctuation throughout the data collection period which was associated with potential socio-environmental triggers. We did not detect any adverse events attributable to the use of the device.

CONCLUSION

This N-of-1 study suggests the Emerald device is feasible to use and can potentially yield actionable data regarding behavioral symptom management. No active or potential device risks were encountered.

Personalising Management of Behavioural and Psychological Symptoms of Dementia in Nursing Homes: Exploring the Synergy of Quantitative and Qualitative Data

Researchers have been exploring how to manage Behavioural and Psychological Symptoms of Dementia (BPSD) in a personalised way, meanwhile, assistive technologies have been developed to collect a variety of personal data. This urges more research in investigating the combination of: data collected by the care team, which are mainly qualitative; and data collected by assistive technologies, the majority of which are quantitative. Previous studies, however, have yet to explore if and how a combination of quantitative and qualitative data could facilitate the care team to better understand each resident with dementia in the nursing home context for personalised BPSD management. Guided by a Research through Design approach, a prototype for collecting and visualising the quantitative and qualitative data towards personalised BPSD management was developed together with the care team. Via developing this prototype, knowledge was gained in what types of data could be combined for personalised BPSD management in nursing homes, what are their values, how to collect and present them, and how to introduce them in the working routine of the care team for analysis. The main findings suggest that the types of data to be collected could be unique for each resident with dementia; the quantitative and qualitative data are of value to each other during data collection and analysis; data collection should be quick and standardised yet flexible for the care team; the overview page is vital for data presentation; and user scenarios could be created to nudge the care team to analyse the data at certain points of their working routine. In general, a combination of qualitative data and quantitative data could help the care team to discover more insights about each resident with dementia and thus improve the current practice of personalised BPSD management.

Smart Environments and Social Robots for Age-Friendly Integrated Care Services

The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

Agitation Detection in People Living with Dementia using Multimodal Sensors

People Living with Dementia (PLwD) often exhibit behavioral and psychological symptoms of dementia; with agitation being one of the most prevalent symptoms. Agitated behaviour in PLwD indicates distress and confusion and increases the risk to injury to both the patients and the caregivers. In this paper, we present the use of wearable devices to detect agitation in PLwD. We hypothesize that combining multi-modal sensor data can help in building better classifiers to identify agitation in PLwD in comparison to a single sensor. We present a unique study to collect motion and physiological data from PLwD. This multi-modal sensor data is subsequently used to build predictive models to detect agitation in PLwD. The results on Random Forest for 28 days of data from PLwD show a strong evidence to support our hypothesis and highlight the importance of using multi-modal sensor data for detecting agitation events amongst them.

Quality of Life Framework for Personalised Ageing: A Systematic Review of ICT Solutions

Given the growing number of older people, society as a whole should ideally provide a higher quality of life (QoL) for its ageing citizens through the concept of personalised ageing. Information and communication technologies (ICT) are subject to constant and rapid development, and can contribute to the goal of an improved QoL for older adults. In order to utilise future ICT solutions as a part of an age-friendly smart environment that helps achieve personalised ageing with an increased QoL, one must first determine whether the existing ICT solutions are satisfying the needs of older people. In order to accomplish that, this study contributes in three ways. First, it proposes a framework for the QoL of older adults, in order to provide a systematic review of the state-of-the-art literature and patents in this field. The second contribution is the finding that selected ICT solutions covered by articles and patents are intended for older adults and are validated by them. The third contribution of the study are the six recommendations that are derived from the review of the literature and the patents which would help move the agenda concerning the QoL of older people and personalised ageing with the use of ICT solutions forward.

Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes

INTRODUCTION

Sensor-based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors.

METHODS

We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four-week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen-Mansfield Agitation Inventory.

RESULTS

We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen-Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level.

DISCUSSION

We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long-term measurements in a real-world setting.

Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

Background

The prevalence of dementia is expected to rapidly increase in the next decades, warranting innovative solutions improving diagnostics, monitoring and resource utilization to facilitate smart housing and living in the nursing home. This systematic review presents a synthesis of research on sensing technology to assess behavioral and psychological symptoms and to monitor treatment response in people with dementia.

Methods

The literature search included medical peer-reviewed English language publications indexed in Embase, Medline, Cochrane library and Web of Sciences, published up to the 5^th of April 2019. Keywords included MESH terms and phrases synonymous with "dementia", "sensor", "patient", "monitoring", "behavior", and "therapy". Studies applying both cross sectional and prospective designs, either as randomized controlled trials, cohort studies, and case-control studies were included. The study was registered in PROSPERO 3^rd of May 2019.

Results

A total of 1,337 potential publications were identified in the search, of which 34 were included in this review after the systematic exclusion process. Studies were classified according to the type of technology used, as (1) wearable sensors, (2) non-wearable motion sensor technologies, and (3) assistive technologies/smart home technologies. Half of the studies investigated how temporarily dense data on motion can be utilized as a proxy for behavior, indicating high validity of using motion data to monitor behavior such as sleep disturbances, agitation and wandering. Further, up to half of the studies represented proof of concept, acceptability and/or feasibility testing. Overall, the technology was regarded as non-intrusive and well accepted.

Conclusions

Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. To highlight awareness of legal regulations, data risk assessment, and patient and public involvement, we propose a necessary framework for sustainable ethical innovation in healthcare technology. The success of this field will depend on interdisciplinary cooperation and the advance in sustainable ethic innovation.

Systematic Review Registration

PROSPERO, identifier CRD42019134313.

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

Challenges in Collecting Big Data in A Clinical Environment with Vulnerable Population: Lessons Learned from A Study Using A Multi-modal Sensors Platform

Agitation is one of the most common behavioural and psychological symptoms in people living with dementia (PLwD). This behaviour can cause tremendous stress and anxiety on family caregivers and healthcare providers. Direct observation of PLwD is the traditional way to measure episodes of agitation. However, this method is subjective, bias-prone and timeconsuming. Importantly, it does not predict the onset of the agitation. Therefore, there is a need to develop a continuous monitoring system that can detect and/or predict the onset of agitation. In this study, a multi-modal sensor platform with video cameras, motion and door sensors, wristbands and pressure mats were set up in a hospital-based dementia behavioural care unit to develop a predictive system to identify the onset of agitation. The research team faced several barriers in the development and initiation of the study, namely addressing concerns about the study ethics, logistics and costs of study activities, device design for PLwD and limitations of its use in the hospital. In this paper, the strategies and methodologies that were implemented to address these challenges are discussed for consideration by future researchers who will conduct similar studies in a hospital setting.

Use of Actigraphy to Measure Symptoms of Agitation in Dementia

OBJECTIVE

To evaluate the feasibility and validity of actigraphy as a measurement of agitation in dementia.

METHODS

Participants aged 65 and older, diagnosed with dementia, residing in a geriatric psychiatry inpatient unit or long-term care facility were included in a cross-sectional study. Agitation was assessed using the Cohen-Mansfield Agitation Inventory (CMAI) and the Neuropsychiatric Inventory (NPI). Actigraphy was measured over seven days and compared across groups categorized as low or high agitation based on a CMAI cutoff score of 50.

RESULTS

Twenty participants were enrolled (mean age = 74.3 years, standard deviation [SD] = 8.69). The 24-hour mean motor activity as measured with actigraphy was significantly different between the low and high agitation groups (180.23, SD = 86.34 versus 81.51, SD = 30.29, Z = 2.29; p = 0.02). Most actigraph variables had significant correlations with CMAI and NPI scores.

CONCLUSION

Actigraphy was highly correlated with informant-based methods for measuring agitation in individuals with dementia and actigraphy may be useful tool for measuring agitation.

Women Coping With a Partner's Dementia-Related Violence: A Qualitative Study

PURPOSE

The aim of the present study was to differentiate between the lived experience of two groups of women caregiving for a partner with dementia. One group was coping with lifelong intimate partner violence (IPV) and dementia-related violence (Group 1); the other group was coping with dementia-related violence only (Group 2).

DESIGN

An interpretive phenomenological analysis perspective was used. Data collection was performed through in-depth, semistructured interviews with eight female spouses of men with dementia from each of the two above-mentioned groups, followed by a content analysis.

FINDINGS

Comparing the narratives of the aging women revealed their different experiences over several dimensions: (a) the identification of violence as a symptom of dementia; (b) the use of past couplehood memories; (c) feelings over time; (d) willingness to care for the partner with dementia; and (e) a prospective view of life.

CONCLUSIONS

The complexities of being old and having to cope with caregiving responsibilities for a spouse with dementia, accompanied by violent behaviors, emphasize the significance of the couple's past relationship. This notion should be taken into account in practical interventions.

CLINICAL RELEVANCE

As part of the aging process, there is an increase in people who are engaged in dementia-related violence. Nurses' education and practice should focus on the dynamics of dyads coping with violence and identify the particular needs of the caregiver spouse in this context.

Integrative review: Persistent vocalizations among nursing home residents with dementia

ABSTRACTBackground:Nursing home (NH) residents with dementia commonly exhibit persistent vocalizations (PVs), otherwise known in the literature as disruptive or problematic vocalizations. Having a better understanding of PVs and the research completed to date on this phenomenon is important to guide further research and clinical practice in NHs. This integrative review examines the current literature on the phenomenon of PVs among NH residents with dementia.

METHODS

We conducted a search in the PubMed, Scopus, Ovid Medline, and CINAHL databases for articles published in English. Articles were included if the focus was specifically on research involving vocal behaviors of older adults with dementia residing in NHs.

RESULTS

Our literature search revealed eight research articles that met the inclusion criteria. These studies were published in 2011 or earlier and involved small sample sizes. Seven of these studies were descriptive and the eighth was a non-pharmacological intervention study for PVs exhibited by NH residents with dementia. These studies were vastly different in their labeling, definitions, and categorization of the PVs as well as methods of measuring PVs.

CONCLUSION

The heterogeneity of the evidence limits the ability to make recommendations for practice. Given the paucity of research on this phenomenon; recommendations for additional research are given.

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.