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

Bayesian meta-analysis of phase 3 results of aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease

INTRODUCTION

Phase 3 trials using the anti-amyloid antibodies aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease dementia were heterogeneous in respect to statistical significance of effects. However, heterogeneity of results has not yet directly be quantified.

METHODS

We used Bayesian random effects meta-analysis to quantify evidence for or against a treatment effect, and assessed the size of the effect and its heterogeneity. Data were extracted from published studies where available and Web based data reports, assuming a Gaussian data generation process.

RESULTS

We found moderate evidence in favor of a treatment effect (Bayes factor = 13.2). The effect was moderate to small with -0.33 (95% credible interval -0.54 to -0.10) points on the Clinical Dementia Rating - Sum of Boxes (CDR-SB) scale. The heterogeneity parameter was low to moderate with 0.21 (0.04 to 0.45) CDR-SB points.

DISCUSSION

Heterogeneity across studies was moderate despite some trials reaching statistical significance, while others did not. This suggests that the negative aducanumab and gantenerumab trials are in full agreement with the expected effect sizes.

Assistive technologies and aging in place for people with dementia and disabilities: a proof-of-concept study with in-home passive remote monitoring with interactive communication functions

PURPOSE

Can assistive technologies (ATs) support aging in place for people with dementia and disability? In seeking to go beyond the persistent institutional care delivery paradigm, this proof-of-concept study tested the feasibility of home care delivery using sensors and remote communication devices. This article reports the collaborative efforts among care professionals, care recipients and family caregivers in their private home environment and the impact of in-home passive remote monitoring (PRM) system on the users. The purpose of this study was to investigate the usability and impact of a PRM system combining in-house passive remote monitoring and an interactive communication function.

METHODS

In order to realize AT-supported, person-centered aging in place, a new care delivery model was designed, developed and tested for the duration of 12 weeks. The study was conducted with 5 older people (1 with severe disability and 4 with dementia), their primary family carers with 15 care professionals as users.

RESULTS

The findings indicate that there were some technical issues. However, the overall assessment of the system performance was positive, and the users expressed favorable views regarding its preventive and interactive nature. The importance of team-based care delivery, adjusted to fit the PRM equipment, was also highlighted. Faced with the challenge of meeting the increasing demand for person-centered care with limited resources, there will be a greater need for better integration of improved ATs. The study indicates ATs' potential for enhancing the quality of life for those involved in caregiving, while stressing the significance of stakeholders' engagement, skills and teamwork.IMPLICATIONS FOR REHABILITATIONThis proof-of-concept study tested the feasibility of a home care delivery system using sensors and remote communication device for those with dementia and disabilities.A home care delivery system was successfully created for 12 weeks by collaborative efforts among care professionals, care recipients and family caregivers in their private home environment.The introduction of in-home passive remote monitoring system increased the possibility of the older adults being able to live independently, and enabled rehabilitation at home.The users had favourable views regarding the system's preventive and interactive nature and highlighted a greater need for better integration of improved assistive technology in long-term care and rehabilitation.

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment

Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Examining the use of cannabidiol and delta-9-tetrahydrocannabinol-based medicine among individuals diagnosed with dementia living within residential aged care facilities: Results of a double-blind randomised crossover trial

OBJECTIVE

Dementia affects individuals older than 65 years. Currently, residential aged care facilities (RACF) use psychotropic medications to manage behavioural and neuropsychiatric symptoms of dementia (BPSD), which are recommended for short-term use and have substantial side effects, including increased mortality. Cannabinoid-based medicines (CBM) have some benefits that inhibit BPSD and cause minimal adverse effects (AEs), yet limited research has been considered with this population. The study aimed to determine a tolerable CBM dose (3:2 delta-9-tetrahydrocannabinol:cannabidiol), and assessed its effect on BPSD, quality of life (QoL) and perceived pain.

METHODS

An 18-week randomised, double-blinded, crossover trial was conducted. Four surveys, collected on seven occasions, were used to measure changes in BPSD, QoL and pain. Qualitative data helped to understand attitudes towards CBM. General linear mixed models were used in the analysis, and the qualitative data were synthesised.

RESULTS

Twenty-one participants (77% female participants, mean age 85) took part in the trial. No significant differences were seen between the placebo and CBM for behaviour, QOL or pain, except a decrease in agitation at the end of treatment in favour of CBM. The qualitative findings suggested improved relaxation and sleep among some individuals. Post hoc estimates on the data collected suggested that 50 cases would draw stronger conclusions on the Neuropsychiatric Inventory.

CONCLUSIONS

The study design was robust, rigorous and informed by RACF. The medication appeared safe, with minimal AEs experienced with CBM. Further studies incorporating larger samples when considering CBM would allow researchers to investigate the sensitivity of detecting BPSD changes within the complexity of the disease and concomitant with medications.

Using digital technology to quantify habitual physical activity in community-dwellers with cognitive impairment: A systematic review (Preprint)

BACKGROUND

Participating in habitual physical activity (HPA) can support people with dementia and mild cognitive impairment (MCI) to maintain functional independence. Digital technology can continuously measure HPA objectively, capturing nuanced measures relating to its volume, intensity, pattern, and variability.

OBJECTIVE

To understand HPA participation in people with cognitive impairment, this systematic review aims to (1) identify digital methods and protocols; (2) identify metrics used to assess HPA; (3) describe differences in HPA between people with dementia, MCI, and controls; and (4) make recommendations for measuring and reporting HPA in people with cognitive impairment.

METHODS

Key search terms were input into 6 databases: Scopus, Web of Science, Psych Articles, PsychInfo, MEDLINE, and Embase. Articles were included if they included community dwellers with dementia or MCI, reported HPA metrics derived from digital technology, were published in English, and were peer reviewed. Articles were excluded if they considered populations without dementia or MCI diagnoses, were based in aged care settings, did not concern digitally derived HPA metrics, or were only concerned with physical activity interventions. Key outcomes extracted included the methods and metrics used to assess HPA and differences in HPA outcomes across the cognitive spectrum. Data were synthesized narratively. An adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess the quality of articles. Due to significant heterogeneity, a meta-analysis was not feasible.

RESULTS

A total of 3394 titles were identified, with 33 articles included following the systematic review. The quality assessment suggested that studies were moderate-to-good quality. Accelerometers worn on the wrist or lower back were the most prevalent methods, while metrics relating to volume (eg, daily steps) were most common for measuring HPA. People with dementia had lower volumes, intensities, and variability with different daytime patterns of HPA than controls. Findings in people with MCI varied, but they demonstrated different patterns of HPA compared to controls.

CONCLUSIONS

This review highlights limitations in the current literature, including lack of standardization in methods, protocols, and metrics; limited information on validity and acceptability of methods; lack of longitudinal research; and limited associations between HPA metrics and clinically meaningful outcomes. Limitations of this review include the exclusion of functional physical activity metrics (eg, sitting/standing) and non-English articles. Recommendations from this review include suggestions for measuring and reporting HPA in people with cognitive impairment and for future research including validation of methods, development of a core set of clinically meaningful HPA outcomes, and further investigation of socioecological factors that may influence HPA participation.

TRIAL REGISTRATION

PROSPERO CRD42020216744; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216744  .

DMapp: a developing promising approach to monitor symptoms progression and stimulate memory in Italian people with cognitive impairments

BACKGROUND

Based on recent researches on the use of natural language processing techniques for very early detection of cognitive decline and the benefits of cognitive stimulation for people with cognitive impairments, the Dementia Monitoring application (DMapp) is developed inside the Memento project.

AIMS

The aims of this work are: (1) to present DMapp; (2) to report the results of two preliminary studies on DMapp; (3) to describe the clinical and experimental potentiality of DMapp.

METHODS

Italian people with the diagnosis of mild cognitive impairment due to Alzheimer's disease or dementia due to Alzheimer's Disease with a Mini-Mental-State-Examination between 24 and 28 (inclusive) were involved in the DMapp development prototype during the Lab Trial (4 subjects) and Filed Trial (5 subjects) of the Memento project. Qualitative and quantitative data were collected to evaluate participants' opinions, the DMapp ability to perform the automatic analysis of the speech and participants' visible emotional state effective. Ad hoc interviews, the Observed Emotion Rating Scale and performance metrics to solve different tasks were used. The relation between cognitive measures (global cognitive measures) and linguistic indexes values was considered using Euclidean distances between the participants.

RESULTS

Linguistic indexes were calculated and seemed to classify the participants' performance as expected from cognitive measures. The DMapp was appreciated by people with cognitive impairment. Positive emotions were present.

CONCLUSION

DMapp seems an interesting approach to monitor dementia symptoms progression and stimulate memory. Possible developments and open questions are discussed.

Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools

As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach—digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults.

Alzheimer Disease: Standard of Diagnosis, Treatment, Care, and Prevention

Alzheimer disease (AD) is the most frequent cause of dementia in people 60 y old or older. This white paper summarizes the current standards of AD diagnosis, treatment, care, and prevention. Cerebrospinal fluid and PET measures of cerebral amyloidosis and tauopathy allow the diagnosis of AD even before dementia (prodromal stage) and provide endpoints for treatments aimed at slowing the AD course. Licensed pharmacologic symptomatic drugs enhance cholinergic pathways and moderate excess of glutamatergic transmission to stabilize cognition. Disease-modifying experimental drugs moderate or remove brain amyloidosis, but so far with modest clinical effects. Nonpharmacologic interventions and a healthy lifestyle (diet, socioaffective inclusion, cognitive stimulation, physical exercise, and others) provide some beneficial effects. Prevention targets mainly modifiable dementia risk factors such as unhealthy lifestyle, cardiovascular-metabolic and sleep-wake cycle abnormalities, and mental disorders. A major challenge for the future is telemonitoring in the real world of these modifiable risk factors.

A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study

Background

Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring.

Objective

The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD.

Methods

This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic.

Results

Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90).

Conclusions

These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.

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.

Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis

BACKGROUND

Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.

METHODS

Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

RESULTS

Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.

CONCLUSIONS

Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.

State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review

In the last decade, there has been a significant increase in the number of people diagnosed with dementia. With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. However, existing devices may not fully meet these needs due to fears and uncertainties about their use, educational support, and finances. Further challenges have been created by COVID-19 and the need for improved safety and security. We have performed a systematic review by exploring several databases describing assistive technologies for dementia and identifying relevant publications for this review. We found there is significant need for appropriate user testing of such devices and have highlighted certifying bodies for this purpose. Given the safety measures imposed by the COVID-19 pandemic, this review identifies the benefits and challenges of existing assistive technologies for people living with dementia and their caregivers. It also provides suggestions for future research in these areas.

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).

Development of the systematic observation of COVID-19 mitigation (SOCOM): Assessing face covering and distancing in schools

INTRODUCTION

During the COVID-19 pandemic, some K-12 schools resumed in-person classes with varying degrees of mitigation plans in the fall 2020. Physical distancing and face coverings can minimize SARS-CoV-2 spread, the virus that causes COVID-19. However, no research has focused on adherence to mitigation strategies during school days. Thus, we sought to develop a systematic observation protocol to capture COVID-19 mitigation strategy adherence in school environments: The Systematic Observation of COVID-19 Mitigation (SOCOM).

METHODS

We extended previously validated and internationally used tools to develop the SOCOM training and implementation protocols to assess physical-distancing and face-covering behaviors. SOCOM was tested in diverse indoor and outdoor settings (classrooms, lunchrooms, physical education [PE], and recess) among diverse schools (elementary, secondary, and special needs).

RESULTS

For the unique metrics of physical-distancing and face-covering behaviors, areas with less activity and a maximum of 10-15 students were more favorable for accurately capturing data. Overall proportion of agreement was high for physical distancing (90.9%), face covering (88.6%), activity type (89.2%), and physical activity level (87.9%). Agreement was lowest during active recess, PE, and observation areas with ≥20 students.

CONCLUSIONS

Millions of children throughout the USA are likely to return to school in the months ahead. SOCOM is a relatively inexpensive research tool that can be implemented by schools to determine mitigation strategy adherence and to assess protocols that allow students return to school safely and slow the spread of COVID-19.

Stem Cell Therapies in Alzheimer's Disease: Applications for Disease Modeling

Alzheimer's disease (AD) is a neurodegenerative disease with complex pathologic and biologic characteristics. Extracellular β-amyloid deposits, such as senile plaques, and intracellular aggregation of hyperphosphorylated tau, such as neurofibrillary tangles, remain the main neuropathological criteria for the diagnosis of AD. There is currently no effective treatment of the disease, and many clinical trials have failed to prove any benefits of new therapeutics. More recently, there has been increasing interest in harnessing the potential of stem cell technologies for drug discovery, disease modeling, and cell therapies, which have been used to study an array of human conditions, including AD. The recently developed and optimized induced pluripotent stem cell (iPSC) technology is a critical platform for screening anti-AD drugs and understanding mutations that modify AD. Neural stem cell (NSC) transplantation has been investigated as a new therapeutic approach to treat neurodegenerative diseases. Mesenchymal stem cells (MSCs) also exhibit considerable potential to treat neurodegenerative diseases by secreting growth factors and exosomes, attenuating neuroinflammation. This review highlights recent progress in stem cell research and the translational applications and challenges of iPSCs, NSCs, and MSCs as treatment strategies for AD. Even though these treatments are still in relative infancy, these developing stem cell technologies hold considerable promise to combat AD and other neurodegenerative disorders. SIGNIFICANCE STATEMENT: Alzheimer's disease (AD) is a neurodegenerative disease that results in learning and memory defects. Although some drugs have been approved for AD treatment, fewer than 20% of patients with AD benefit from these drugs. Therapies based on stem cells, including induced pluripotent stem cells, neural stem cells, and mesenchymal stem cells, provide promising therapeutic strategies for AD.

The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes

Laboratory-based gait assessments are indicative of clinical outcomes (e.g., disease identification). Real-world gait may be more sensitive to clinical outcomes, as impairments may be exaggerated in complex environments. This study aims to investigate how different environments (e.g., lab, real world) impact gait. Different walking bout lengths in the real world will be considered proxy measures of context. Data collected in different dementia disease subtypes will be analysed as disease-specific gait impairments are reported between these groups. Thirty-two people with cognitive impairment due to Alzheimer’s disease (AD), 28 due to dementia with Lewy bodies (DLB) and 25 controls were recruited. Participants wore a tri-axial accelerometer for six 10 m walks in lab settings, and continuously for seven days in the real world. Fourteen gait characteristics across five domains were measured (i.e., pace, variability, rhythm, asymmetry, postural control). In the lab, the DLB group showed greater step length variability (p = 0.008) compared to AD. Both subtypes demonstrated significant gait impairments (p < 0.01) compared to controls. In the real world, only very short walking bouts (<10 s) demonstrated different gait impairments between subtypes. The context where walking occurs impacts signatures of gait impairment in dementia subtypes. To develop real-world gait assessment as a clinical tool, algorithms and metrics must accommodate for changes in context.

Wayfinding in People with Alzheimer’s Disease: Perspective Taking and Architectural Cognition—A Vision Paper on Future Dementia Care Research Opportunities

Based on a targeted literature review, this vision paper emphasizes the importance of dementia-sensitive built space. The article specifically focuses on supporting spatial orientation and wayfinding for people living with dementia. First, we discuss types of wayfinding challenges, underlying processes, and consequences of spatial disorientation in the context of dementia of the Alzheimer’s type. Second, we focus on current efforts aimed at planning and evaluating dementia-sensitive built space, i.e., environmental design principles, interventions, evaluation tools, strategies, and planning processes. Third, we use our findings as a starting point for developing an interdisciplinary research vision aimed at encouraging further debates and research about: (1) the perspective of a person with dementia, specifically in the context of wayfinding and spatial orientation, and (2) how this perspective supplements planning and design processes of dementia-sensitive built space. We conclude that more closely considering the perspective of people with dementia supports the development of demographically sustainable future cities and care institutions.

The use of wearable/portable digital sensors in Huntington's disease: A systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington's disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD.

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Wearable/portable sensors have been proposed to detect and quantify manifestations of many neurodegenerative diseases.

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No systematic review so far has examined their use in Huntington's disease (HD).

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This work draws a broad picture of the digital wearable-based landscape in HD.

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The utility of wearables in clinical practice and therapeutic research still needs to be proved.

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Collaborative efforts are needed to further investigate their clinical use in HD.

Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges

Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.

The Neuroprotective and Neurodegeneration Effects of Heme Oxygenase-1 in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by complex pathological and biological features. Notably, extracellular amyloid-β deposits as senile plaques and intracellular aggregation of hyperphosphorylated tau as neurofibrillary tangles remain the primary premortem criterion for the diagnosis of AD. Currently, there exist no disease-modifying therapies for AD, and many clinical trials have failed to show its benefits for patients. Heme oxygenase 1 (HO-1) is a 32 kDa enzyme, which catalyzes the degradation of cellular heme to free ferrous iron, biliverdin, and carbon monoxide under stressful conditions. Several studies highlight the crucial pathological roles of HO-1 in the molecular processes of AD. The beneficial roles of HO-1 overexpression in AD brains are widely accepted due to its ability to convert pro-oxidant heme to biliverdin and bilirubin (antioxidants), which promote restoration of a suitable tissue redox microenvironment. However, the intracellular oxidative stress might be amplified by metabolites of HO-1 and exacerbate the progression of AD under certain circumstances. Several lines of evidence have demonstrated that upregulated HO-1 is linked to tauopathies, neuronal damage, and synapse aberrations in AD. Here, we review the aspects of the molecular mechanisms by which HO-1 regulates AD and the latest information on the pathobiology of AD. We further highlight the neuroprotective and neurodystrophic actions of HO-1 and the feasibility of HO-1 as a therapeutic target for AD.

Smart home technology solution for night-time wandering in persons with dementia

Introduction

More than half of persons with dementia will experience night-time wandering, increasing their risk of falls and unattended home exits. This is a major predictor of caregiver burnout and one of the major causes of early institutionalization.

Methods

Using smart home technologies such as sensors, smart bulbs, pressure mats and speakers, the Night-time Wandering Detection and Diversion system is designed to assist caregivers and persons with dementia that are at risk of wandering at night. Being placed in homes around Ottawa for a 12-week trial, the system allows caregivers to rest peacefully in the night, as it detects when the person with dementia gets out of bed and automatically provides cue lighting to guide them safely to the washroom. The system also uses prerecorded audio prompts, if they venture from the bedroom, only waking the caregiver when the person with dementia opens an exit door.

Results

Thus far, the average depression and anxiety in caregivers have been improved after the 12 weeks, and most have said that they sleep more peacefully.

Conclusion

The system has proven successful in supporting the safety of persons with dementia as well as their caregivers.

Aggression Prevention Training for Individuals With Dementia and Their Caregivers: A Randomized Controlled Trial

OBJECTIVE

International appeals call for interventions to prevent aggression and other behavioral problems in individuals with dementia (IWD). Aggression Prevention Training (APT), based on intervening in three contributors to development of aggression (IWD pain, IWD depression, and caregiver-IWD relationship problems) aims to reduce incidence of aggression in IWD over 1 year.

DESIGN

Randomized, controlled trial.

SETTING

Three clinics that assess, diagnose, and treat dementia.

PARTICIPANTS

Two hundred twenty-eight caregiver-IWD dyads who screened positive for IWD pain, IWD depression, or caregiver-IWD relationship problems randomized to APT or Enhanced Usual Primary Care (EU-PC).

INTERVENTION

APT, a skills-based intervention delivered over 3 months to address pain/depression/caregiver-IWD relationship issues. EU-PC included printed material on dementia and community resources; and eight brief, weekly support calls.

MEASUREMENTS

The primary outcome was incidence of aggression over 1 year, determined by the Cohen Mansfield Agitation Inventory-Aggression Subscale. Secondary outcomes included pain, depression, caregiver-IWD relationship, caregiver burden, positive caregiving, behavior problems, and anxiety.

RESULTS

Aggression incidence and secondary outcomes did not differ between groups. However, in those screening positive for IWD depression or caregiver-IWD relationship problems, those receiving EU-PC had significant increases in depression and significant decreases in quality of the caregiver-IWD relationship, whereas those receiving APT showed no changes in these outcomes over time.

CONCLUSION

The cost to patients, family, and society of behavioral problems in IWD, along with modest efficacy of most pharmacologic and nonpharmacologic interventions, calls for more study of novel preventive approaches.

Fluctuating cognition in the Lewy body dementias

Fluctuating cognition is a core diagnostic feature of dementia with Lewy bodies and is also a key clinical feature of Parkinson's disease dementia. These dementias share common pathological features and are referred to as Lewy body dementias. Whilst highly prevalent in Lewy body dementia, with up to 90% of patients experiencing the symptom at some point in the disease trajectory, clinical identification of fluctuating cognition is often challenging. Furthermore, its underlying pathophysiological processes remain unclear. However, neuroimaging and neurophysiological techniques have recently provided insight into potential drivers of the phenomenon. In this update, we review data pertaining to clinical features and underlying mechanisms of fluctuating cognition in Lewy body dementia. We collate evidence for different proposed aetiologies: fluctuating cognition as an attentional disorder, as a consequence of loss of cholinergic drive, as a manifestation of failure in neuronal efficiency and synchrony, and as a disorder of sleep/arousal. We also review data relating to putative mechanisms that have received less attention to date. Increased understanding of fluctuating cognition may help to illuminate pathophysiological mechanisms in cognitive processing in Lewy body dementia, guide future research, and facilitate the design of targeted therapeutic approaches.

O. Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

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.

Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?

BACKGROUND

There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology.

RESEARCH QUESTION

This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis.

METHODS

80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics.

RESULTS

The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics.

SIGNIFICANCE

This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.

Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From Regular Monitoring Service

BACKGROUND

Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored.

OBJECTIVE

The objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations.

METHODS

We analyzed daily conversational data of seniors with and without AD obtained from longitudinal follow-up in a regular monitoring service (from n=15 individuals including 2 AD patients at an average follow-up period of 16.1 months; 1032 conversational data items obtained during phone calls and approximately 221 person-hours). In addition to the standard linguistic features used in previous studies on connected speech data during neuropsychological tests, we extracted novel features related to atypical repetition of words and topics reported by previous observational and descriptive studies as one of the prominent characteristics in everyday conversations of AD patients.

RESULTS

When we compared the discriminative power for AD, we found that atypical repetition in two conversations on different days outperformed other linguistic features used in previous studies on speech data during neuropsychological tests. It was also a better indicator than atypical repetition in single conversations as well as that in two conversations separated by a specific number of conversations.

CONCLUSIONS

Our results show how linguistic features related to atypical repetition across days could be used for detecting AD from daily conversations in a passive manner by taking advantage of longitudinal data.

Predicting dementia with routine care EMR data

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.

Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia

Health care systems face an expansion in the number of older individuals with a high prevalence of neurodegenerative diseases and related behavioral and psychological symptoms of dementia (BPSDs). Health care providers are expected to develop innovative solutions to manage and follow up patients over time in the community. To date, we are unable to continuously and accurately monitor the nature, frequency, severity, impact, progression, and response to treatment of BPSDs after the initial assessment. Technology could address this need and provide more sensitive, less biased, and more ecologically valid measures. This could provide an opportunity to reevaluate therapeutic strategies more quickly and, in some cases, to treat earlier, when symptoms are still amenable to therapeutic solutions or even prevention. Several studies confirm the relationship between sensor-based data and cognition, mood, and behavior. Most scientific work on mental health and technologies supports digital biomarkers, not so much as diagnostic tools but rather as monitoring tools, an area where unmet needs are significant. In addition to the implications for clinical care, these real-time measurements could lead to the discovery of new early biomarkers in mental health. Many also consider digital biomarkers as a way to better understand disease processes and that they may contribute to more effective pharmaceutical research by (i) targeting the earliest stage, (ii) reducing sample size required, (iii) providing more objective measures of behaviors, (iv) allowing better monitoring of noncompliance, (v) and providing a better understanding of failures. Finally, communication technologies provide us with the opportunity to support and renew our clinical and research practices.

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.

Technology and Dementia: The Future is Now

BACKGROUND

Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place.

OBJECTIVES

To summarise key areas of technology development in dementia and identify future directions and implications.

METHOD

Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia.

RESULTS

The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management.

CONCLUSIONS

The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.

Inertial wearables as pragmatic tools in dementia

Dementia is a critically important issue due to its wide impact on health services as well as its personal and societal costs. Limitations exist for current dementia protocols, and there are calls to introduce modern technology that facilitates the addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data on habitual activities, but their utility in modern medicine remains low. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology; they offer a low-cost approach to quantify routine movements that are fundamental to normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to aid dementia research. We outline the history of wearables, from their humble beginnings to their current use beyond the clinic, and explore their integration into modern systems, as well as the ongoing standardisation and regulatory efforts to integrate their use in clinical trials.