Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living

Detecting mild cognitive impairment by applying integrated random forest to finger tapping

Early detection of dementia is essential to reduce the decline in quality of life (QoL) and the increase in medical and nursing care costs associated with dementia in an aging society. In this study, we aimed to develop a simple screening test for mild cognitive impairment (MCI), a preliminary stage of dementia, by creating an analytical method to accurately detect MCI through finger-tapping measurement. We extracted 248 characteristics from the finger-tapping waveforms of 182 MCI patients and 352 normal controls, applying five conventional classification methods along with an improved Random Forest (RF) method proposed in this study (Integrated RF). In the proposed method, the RF classification model for the MCI and normal control groups is supplementally integrated with the RF classification model for the Alzheimer's disease and normal control groups to generate a new classification model. When comparing the discrimination accuracy of each method, the proposed method achieved the highest accuracy, with an F1-score of 0.795 (recall = 0.778 and precision = 0.814). These results demonstrate the potential of finger-tapping measurement as a highly accurate screening test for MCI.

Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Cognition and Activity of Daily Living Function in people with Parkinson's disease

The ability to perform activities of daily living (ADL) function is a multifaceted construct that reflects functionality in different daily life situations. The loss of ADL function due to cognitive impairment is the core feature for the diagnosis of Parkinson's disease dementia (PDD). In contrast to Alzheimer's disease, ADL impairment in PD can be compromised by various factors, including motor and non-motor aspects. This narrative review summarizes the current state of knowledge on the association of cognition and ADL function in people with PD and introduces the concept of "cognitive ADL" impairment for those problems in everyday life that are associated with cognitive deterioration as their primary cause. Assessment of cognitive ADL impairment is challenging because self-ratings, informant-ratings, and performance-based assessments seldomly differentiate between "cognitive" and "motor" aspects of ADL. ADL function in PD is related to multiple cognitive domains, with attention, executive function, and memory being particularly relevant. Cognitive ADL impairment is characterized by behavioral anomalies such as trial-and-error behavior or task step omissions, and is associated with lower engagement in everyday behaviors, as suggested by physical activity levels and prolonged sedentary behavior. First evidence shows that physical and multi-domain interventions may improve ADL function, in general, but the evidence is confounded by motor aspects. Large multicenter randomized controlled trials with cognitive ADL function as primary outcome are needed to investigate which pharmacological and non-pharmacological interventions can effectively prevent or delay deterioration of cognitive ADL function, and ultimately the progression and conversion to PDD.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study

Introduction

Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.

Methods

A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.

Results

The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.

Discussion

This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment

OBJECTIVE

Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA).

METHOD

Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors.

RESULTS

ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040.

CONCLUSION

Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.

Artificial intelligence for dementia-Applied models and digital health

INTRODUCTION

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

METHODS

This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

RESULTS

This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.

DISCUSSION

Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Using home monitoring technology to study the effects of traumatic brain injury in older multimorbid adults

Internet of things (IOT) based in-home monitoring systems can passively collect high temporal resolution data in the community, offering valuable insight into the impact of health conditions on patients' day-to-day lives. We used this technology to monitor activity and sleep patterns in older adults recently discharged after traumatic brain injury (TBI). The demographics of TBI are changing, and it is now a leading cause of hospitalisation in older adults. However, research in this population is minimal. We present three cases, showcasing the potential of in-home monitoring systems in understanding and managing early recovery in older adults following TBI.

Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review

BACKGROUND

Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging.

OBJECTIVE

This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time.

METHODS

A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains-culturally specific tasks and socialization and communication-were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time.

CONCLUSIONS

Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861.

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

INTRODUCTION

The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.

METHODS AND ANALYSIS

The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.

ETHICS AND DISSEMINATION

Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI.

Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review

The management of chronic diseases requires personalized healthcare that allows older adults to manage their diseases at home. This systematic review aimed to describe the smart home technologies used in the management of chronic diseases in older people. A systematic literature review was conducted on four databases and was reported following the PRISMA statement. Nineteen articles were included. The intervention technologies were classified into three groups: smart home, characterized by environmental sensors detecting motion, contact, light, temperature, and humidity; external memory aids, characterized by a partnership between mobile apps and smart home-based activity learning; and hybrid technology, with the integration of multiple technologies, such as devices installed at patients' homes and telemedicine. The health outcomes evaluated are vital signs, medication management, ADL-IADL, mobility, falls, and quality of life. Smart homes show great potential in the management of chronic diseases by favouring the control of exacerbations and increasing patients' safety by providing support in disease management, including support for cognitively impaired older people. The use of smart homes in the community could bring numerous benefits in terms of continuity of care, allowing the constant monitoring of older people by local and hospital health services.

Leading mediators of sex differences in the incidence of dementia in community-dwelling adults in the UK Biobank: a retrospective cohort study

BACKGROUND

Little is known regarding whether sex assigned at birth modifies the association between several predictive factors for dementia and the risk of dementia itself.

METHODS

Our retrospective cohort study included 214,670 men and 214,670 women matched by age at baseline from the UK Biobank. Baseline data were collected between 2006 and 2010, and incident dementia was ascertained using hospital inpatient or death records until January 2021. Mediation analysis was tested for 133 individual factors.

RESULTS

Over 5,117,381 person-years of follow-up, 5928 cases of incident all-cause dementia (452 cases of young-onset dementia, 5476 cases of late-onset dementia) were documented. Hazard ratios (95% CI) for all-cause, young-onset, and late-onset dementias associated with the male sex (female as reference) were 1.23 (1.17-1.29), 1.42 (1.18-1.71), and 1.21 (1.15-1.28), respectively. Out of 133 individual factors, the strongest mediators for the association between sex and incident dementia were multimorbidity risk score (percentage explained (95% CI): 62.1% (45.2-76.6%)), apolipoprotein A in the blood (25.5% (15.2-39.4%)), creatinine in urine (24.9% (16.1-36.5%)), low-density lipoprotein cholesterol in the blood (23.2% (16.2-32.1%)), and blood lymphocyte percentage (21.1% (14.5-29.5%)). Health-related conditions (percentage (95% CI) explained: 74.4% (51.3-88.9%)) and biomarkers (83.0% (37.5-97.5%)), but not lifestyle factors combined (30.1% (20.7-41.6%)), fully mediated sex differences in incident dementia. Health-related conditions combined were a stronger mediator for late-onset (75.4% (48.6-90.8%)) than for young-onset dementia (52.3% (25.8-77.6%)), whilst lifestyle factors combined were a stronger mediator for young-onset (42.3% (19.4-69.0%)) than for late-onset dementia (26.7% (17.1-39.2%)).

CONCLUSIONS

Our analysis matched by age has demonstrated that men had a higher risk of all-cause, young-onset, and late-onset dementias than women. This association was fully mediated by health-related conditions or blood/urinary biomarkers and largely mediated by lifestyle factors. Our findings are important for understanding potential mechanisms of sex in dementia risk.

Broadened assessments, health education and cognitive aids in the remote memory clinic

The prevalence of dementia is increasing and poses a health challenge for individuals and society. Despite the desire to know their risks and the importance of initiating early therapeutic options, large parts of the population do not get access to memory clinic-based assessments. Remote memory clinics facilitate low-level access to cognitive assessments by eschewing the need for face-to-face meetings. At the same time, patients with detected impairment or increased risk can receive non-pharmacological treatment remotely. Sensor technology can evaluate the efficiency of this remote treatment and identify cognitive decline. With remote and (partly) automatized technology the process of cognitive decline can be monitored but more importantly also modified by guiding early interventions and a dementia preventative lifestyle. We highlight how sensor technology aids the expansion of assessments beyond cognition and to other domains, e.g., depression. We also illustrate applications for aiding remote treatment and describe how remote tools can facilitate health education which is the cornerstone for long-lasting lifestyle changes. Tools such as transcranial electric stimulation or sleep-based interventions have currently mostly been used in a face-to-face context but have the potential of remote deployment-a step already taken with memory training apps. Many of the presented methods are readily scalable and of low costs and there is a range of target populations, from the worried well to late-stage dementia.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment

BACKGROUND

The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI).

METHODS

One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory.

RESULTS

Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03).

CONCLUSIONS

The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

Toward Sensor-Based Early Diagnosis of Cognitive Impairment using Poisson Process Models

Sensor-based assessment in combination with machine learning algorithms provide the potential to augment current practices of the (early) diagnosis of cognitive impairment. The goal of this paper is to detect cognitive impairment in elderly adults using sensor-based measures installed in the home. Longitudinal time-series data of sensor signals are analyzed with Poisson process (PP) models and supervised machine learning algorithms to identify individuals with mild cognitive impairment (MCI) and dementia. We examine two types of PP models: a homogeneous PP which assumes a constant rate of change for each sensor, and a non-homogeneous PP which incorporates contextual information by separately estimating the arrival rate for each task. Our results indicate that the proposed approach can effectively distinguish between patients with dementia and healthy individuals, as well as patients with MCI and healthy individuals based on the sensor-based PP features. Sensor-based assessment that relies on the non-homogeneous PP is further found to be more effective for the task of interest compared to homogeneous PP, as well as expert-based assessment. Findings from this research have the potential to help detect the early onset of cognitive impairment in elderly adults, and demonstrate the ability of computational models and machine learning to predict cognitive health, thus, contributing toward advancing aging-in-place. Clinical Relevance-This examines a computational method to quantify cognitive decline for elderly adults using home-based sensors. eventually contributing to ambulatory clinical biomarkers for dementia.

Are Smart Homes Adequate for Older Adults with Dementia?

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users’ daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients’ habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients’ habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users’ behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

Eigenbehaviour as an Indicator of Cognitive Abilities

With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively—it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

Association of social isolation and smartphone use on cognitive functions

Background

The number of socially isolated older adults has increased owing to the coronavirus disease pandemic, thus leading to a decrease in cognitive functions among this group. Smartphone use is expected to be a reasonable preventive measure against cognitive decline in this social context. Thus, this study aimed to investigate the influence of social isolation and smartphone use on cognitive functions in community-dwelling older adults.

Methods

We divided 4,601 community-dwelling older adults into four groups based on their levels of social isolation and smartphone use. Then, we conducted cognitive functions tests including a word list memory task, trail-making test, and symbol digit substitution task. Social isolation was defined when participants met two or more of the following measures: domestic isolation, less social contact, and social disengagement. We used an analysis of covariance adjusted by background information to measure between-group differences in levels of cognitive functions and social isolation. A linear regression model was used to analyze the association of standardized scores of cognitive function tests with smartphone use.

Results

Smartphone users’ scores of the symbol digit substitution task were superior compared with both non-users with social isolation and without. All cognitive functions were associated with smartphone use among non-socially and socially isolated participants. Socially isolated older adults showed an association only between trail making test- part A and smartphone use.

Conclusions

Smartphone use was associated with cognitive functions (memory, attentional function, executive function, and processing speed) even in socially isolated community-dwelling older adults.

A Multiple Stakeholder Perspective on the Drivers and Barriers for the Implementation of Lifestyle Monitoring Using Infrared Sensors to Record Movements for Vulnerable Older Adults Living Alone at Home: A Qualitative Study

A variety of technologies classified as lifestyle monitoring (LM) allows, by unobtrusive monitoring, for supporting of living alone at home of vulnerable older adults, especially persons with neurocognitive disorders such as dementia. It can detect health deterioration, facilitate early intervention, and possibly help people avoid hospital admission. However, for LM to redeem its intended effects, it is important to be adopted by involved stakeholders such as informal and formal caregivers and care managers. Therefore, the aim of this qualitative study is to understand factors that drive or impede successful implementation of LM for vulnerable older adults, specifically using infrared sensors to record movements, studied from a multiple stakeholder perspective. An open coding process was used to identify key themes of the implementation process. Data were arranged according to a thematic framework based on the normalization process theory (NPT). All stakeholders agreed that LM could lead to various health benefits for older adults using LM. However, some did not perceive the LM system to be cost-efficient and expressed a need for more flexible health care structures for LM to be successfully implemented. All stakeholders acknowledged the fact that LM requires a transition of care and responsibilities, a clear eligibility strategy for clients, and a clear ambassador strategy for health care professionals, as well as reliable technology. This study highlights the complex nature of implementing LM and suggests the need for alignment within constructs of the NPT among stakeholders about new ways of collaboration in supporting living alone at home.

Nonwearable Sensor-Based In-Home Assessment of Subtle Daily Behavioral Changes as a Candidate Biomarker for Mild Cognitive Impairment

Patients show subtle changes in daily behavioral patterns, revealed by traditional assessments (e.g., performance- or questionnaire-based assessments) even in the early stage of Alzheimer's disease (AD; i.e., the mild cognitive impairment (MCI) stage). An increase in studies on the assessment of daily behavioral changes in patients with MCI and AD using digital technologies (e.g., wearable and nonwearable sensor-based assessment) has been noted in recent years. In addition, more objective, quantitative, and realistic evidence of altered daily behavioral patterns in patients with MCI and AD has been provided by digital technologies rather than traditional assessments. Therefore, this study hypothesized that the assessment of daily behavioral changes with digital technologies can replace or assist traditional assessment methods for early MCI and AD detection. In this review, we focused on research using nonwearable sensor-based in-home assessment. Previous studies on the assessment of behavioral changes in MCI and AD using traditional performance- or questionnaire-based assessments are first described. Next, an overview of previous studies on the assessment of behavioral changes in MCI and AD using nonwearable sensor-based in-home assessment is provided. Finally, the usefulness and problems of nonwearable sensor-based in-home assessment for early MCI and AD detection are discussed. In conclusion, this review stresses that subtle changes in daily behavioral patterns detected by nonwearable sensor-based in-home assessment can be early MCI and AD biomarkers.

Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88-0.94]; 0.59[0.48-0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = -0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

A Sensor-Driven Visit Detection System in Older Adults Homes: Towards Digital Late-Life Depression Marker Extraction

Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC=0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool (=-0.87, p=0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.

Assessment of Daily Routine Uniformity in a Smart Home Environment Using Hierarchical Clustering

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.

Physical Activity and its Influencing Factors in Community-Dwelling Older Adults With Dementia: A Path Analysis

Dementia is prevalent in worldwide, and increases the care burden and potential costs. Physical activity (PA) has been increasingly shown to be beneficial for them. This was a cross-sectional observational study aiming to investigate the status of PA among community-dwelling older adults with dementia in Beijing or Hangzhou, China, and verify the relationships between neuropsychiatric symptoms, activities of daily living (ADL), caregivers' fear of patients' falling and their PA using a path analysis approach. The level of PA among 216 included people with dementia was low. PA was related to the neuropsychiatric symptoms, with ADL and caregivers' fear of patients' falling have mediation roles. The findings indicated that person-centered strategies related to the management of these symptoms might be helpful to improve ADL, relieve caregivers' concerns about them falling and consequently foster positive participation in PA.

Beacon-Based Remote Measurement of Social Behavior in ASD Clinical Trials: A Technical Feasibility Assessment

In this work, we propose a Bluetooth low energy (BLE) beacon-based algorithm to enable remote measurement of the social behavior of the participants of an observational Autism Spectrum Disorder (ASD) clinical trial (NCT03611075). We have developed a mobile application for a smartphone and a smartwatch to collect beacon signals from BLE beacon sensors as well as to store information about the participants’ household rooms. Our goal is to collect beacon information about the time the participants spent in different rooms of their household to infer sociability information. We applied the same technology and setup in an internal experiment with healthy volunteers to evaluate the accuracy of the proposed algorithm in 10 different home setups, and we observed an average accuracy of 97.2%. Moreover, we show that it is feasible for the clinical study participants/caregivers to set up the BLE beacon sensors in their homes without any technical help, with 96% of them setting up the technology on the first day of data collection. Next, we present results from one-week location data from study participants collected through the proposed technology. Finally, we provide a list of good practice guidelines for optimally applying beacon technology for indoor location monitoring. The proposed algorithm enables us to estimate time spent in different rooms of a household that can pave the development of objective sociability features and eventually support decisions regarding drug efficacy in ASD.

Validation of Malayalam Version of Everyday Abilities Scale for India

BACKGROUND

Cognitive impairment is usually associated with impairment in everyday activities. Scales to assess activities of daily living, like the Everyday Abilities Scale for India (EASI), have been employed as screening tools for dementia or major neurocognitive disorder. EASI had not been validated in Malayalam. This study's objective was to validate the Malayalam version of EASI (M-EASI) in those aged ≥60 years.

METHODS

In a study undertaken in a tertiary care center, those aged ≥60 years attending psychiatry, neurology, or geriatric clinic of general medicine departments were evaluated using M-EASI and the Malayalam version of Addenbrooke's Cognitive Examination (M-ACE). A total of 304 participants were recruited for this questionnaire validation. Information for M-EASI was obtained from a reliable informant.

RESULTS

The mean age of the sample was 70.04 years (standard deviation-7.33). The majority of them were males (58.6%) and educated up to primary school (42.4%), while the majority of the informants were sons/daughters/siblings (47.7%) and were females (73.7%). Taking M-ACE scores as the gold standard for diagnosing MNCD according to Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition criteria, there were 162 cases of MNCD and 142 normal controls. Cronbach's α was 0.91. At an optimal cut-off of 4.5, adequate sensitivity (77.8%), and specificity (75.4%) were observed. The positive predictive value was 78.6%, and the negative predictive value, 74.5%.

CONCLUSION

M-EASI has adequate psychometric properties as a screening tool for MNCD.

Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study

Background

Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults.

Objective

In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults.

Methods

We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis.

Results

Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection.

Conclusions

Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

Fall-related cognition, motor function, functional ability, and depression measures in older adults with dementia

BACKGROUND

As the severity of dementia progresses over time, cognition and motor functions such as muscle strength, balance, and gait are disturbed, and they eventually increase the risk of fall in patients with dementia.

OBJECTIVE

To determine the relationship between the fall risk and cognition, motor function, functional ability, and depression in older adults with dementia.

METHODS

Seventy-four older adults diagnosed with dementia were recruited. Clinical measurements included the Fall Risk Scale by Huh (FSH), Korean version of the Mini-Mental State Examination (MMSE-K), hand grip strength (HGS), Tinetti Performance Oriented Mobility Assessment (POMA), 10-m walk test (10-MWT), Korean version of the Modified Barthel Index (MBI-K), and the Geriatric Depression Scale (GDS).

RESUTLS

The MMSE-K was significantly correlated with the FSH, HGS, and the MBI-K, and FSH was significantly correlated with all of the other outcome measures. In particular, the MMSE-K, HGS, POMA, and the MBI-K were negatively correlated with fall history among the FHS sub-items. Additionally, the MMSE sub-item, attention/concentration was associated with the FSH, HGS, POMA, and the MBI-K.

CONCLUSIONS

These findings suggest that falling is significantly related to impaired cognition, reduced muscle strength, impaired balance, gait, and activities of daily living abilities, and depression in older adults with dementia.

Effect of the Information Support Robot on the Daily Activity of Older People Living Alone in Actual Living Environment

Information support robots (ISRs) have the potential to assist older people living alone to have an independent life. However, the effects of ISRs on the daily activity, especially the sleep patterns, of older people have not been clarified; moreover, it is unclear whether the effects of ISRs depend on the levels of cognitive function. To investigate these effects, we introduced an ISR into the actual living environment and then quantified induced changes according to the levels of cognitive function. Older people who maintained their cognitive function demonstrated the following behavioral changes after using the ISR: faster wake-up times, reduced sleep duration, and increased amount of activity in the daytime (p < 0.05, r = 0.77; p < 0.05, r = 0.89, and p < 0.1, r = 0.70, respectively). The results suggest that the ISR is beneficial in supporting the independence of older people living alone since living alone is associated with disturbed sleep patterns and low physical activity. The impact of the ISR on daily activity was more remarkable in the subjects with high cognitive function than in those with low cognitive function. These findings suggest that cognitive function is useful information in the ISR adaptation process. The present study has more solid external validity than that of a controlled environment study since it was done in a personal residential space.

Performance-based IADL evaluation of older adults with cognitive impairment within a smart home: A feasibility study

INTRODUCTION

Mild cognitive impairment (MCI) is characterized by subtle deficits that functional assessment via informant-report measures may not detect. Sensors can potentially detect deficits in everyday functioning in MCI. This study aims to establish feasibility and acceptability of using sensors in a smart home for performance-based assessments of two instrumental activities of daily living (IADLs).

METHODS

Thirty-five older adults (>65 years) performed two IADL tasks in a smart home laboratory equipped with sensors and a web camera. Participants' cognitive states were determined using published criteria including measures of global cognition and comprehensive neuropsychological test batteries. Selected subtasks of the IADL assessment were autonomously captured by the sensors. Total time taken for each task and subtask were computed. A point scoring system captured accuracy and number of attempts. Acceptability of the smart home setup was assessed.

RESULTS

Participants with MCI (n = 21) took longer to complete both tasks than participants with healthy cognition (HC; n = 14), with significant time differences observed only in "Cost calculation." Completion time for IADL tasks and scores correlated in the expected direction with global cognition. Over 95% of the participants found the smart home assessment acceptable and a positive experience.

DISCUSSION

We demonstrated the feasibility and acceptability of the use of unobtrusive commercially available sensors in a smart home for facilitating parts of the objective assessment of IADL in older adults. Future studies need to identify more IADLs that are suitable for semi-automated or automated assessments through the use of simple, low-cost sensors.

Case Report: Ambient Sensor Signals as Digital Biomarkers for Early Signs of Heart Failure Decompensation

Home monitoring systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. More recently, multimodal ambient sensor systems are also used to monitor digital biomarkers to detect clinically relevant health problems over longer time periods. Clinical signs of HF decompensation including increase of heart rate and respiration rate, decreased physical activity, reduced gait speed, increasing toilet use at night and deterioration of sleep quality have a great potential to be detected by non-intrusive contactless ambient sensor systems and negative changes of these parameters may be used to prevent further deterioration and hospitalization for HF decompensation. This is to our knowledge the first report about the potential of an affordable, contactless, and unobtrusive ambient sensor system for the detection of early signs of HF decompensation based on data with prospective data acquisition and retrospective correlation of the data with clinical events in a 91 year old senior with a serious heart problem over 1 year. The ambient sensor system detected an increase of respiration rate, heart rate, toilet use at night, toss, and turns in bed and a decrease of physical activity weeks before the decompensation. In view of the rapidly increasing prevalence of HF and the related costs for the health care systems and the societies, the real potential of our approach should be evaluated in larger populations of HF patients.

Wearable Based Calibration of Contactless In-home Motion Sensors for Physical Activity Monitoring in Community-Dwelling Older Adults

Passive infrared motion sensors are commonly used in telemonitoring applications to monitor older community-dwelling adults at risk. One possible use case is quantification of in-home physical activity, a key factor and potential digital biomarker for healthy and independent aging. A major disadvantage of passive infrared sensors is their lack of performance and comparability in physical activity quantification. In this work, we calibrate passive infrared motion sensors for in-home physical activity quantification with simultaneously acquired data from wearable accelerometers and use the data to find a suitable correlation between in-home and out-of-home physical activity. We use data from 20 community-dwelling older adults that were simultaneously provided with wireless passive infrared motion sensors in their homes, and a wearable accelerometer for at least 60 days. We applied multiple calibration algorithms and evaluated results based on several statistical and clinical metrics. We found that using even relatively small amounts of wearable based ground-truth data over 7-14 days, passive infrared based wireless sensor systems can be calibrated to give largely better estimates of older adults' daily physical activity. This increase in performance translates directly to stronger correlations of measured physical activity levels with a variety of age relevant health indicators and outcomes known to be associated with physical activity.

Clinical application of the experimental ADL test for patients with cognitive impairment: pilot study

We employed a hospital-based Internet of Things (IoT) platform to validate the role of real-time activities of daily living (ADL) measurement as a digital biomarker for cognitive impairment in a hospital setting. Observational study. 12 patients with dementia, 11 patients with mild cognitive impairment (MCI), and 15 cognitively normal older adults. The results of 13 experimental ADL tasks were categorized into success or fail. The total number of successful task and the average success proportion of each group was calculated. Time to complete the total tasks was also measured. Patients with dementia, patients with MCI, and cognitively normal older adults performed 13 experimental ADL tasks in a hospital setting. Significant differences in the average success rate of 13 tasks were found among groups. Dementia group showed the lowest success proportion (49.3%) compared with MCI group (78.3%) and normal group (97.4%). Correlation between classical ADL scales and the number of completed ADL tasks was statistically significant. In particular, instrumental ADL (I-ADL) had stronger relationship with the number of completed ADL tasks than Barthel's ADL (B-ADL). Dementia group required more time to accomplish the tasks when compared to MCI and normal groups. This study demonstrated that there is a clear relationship between the performance of experimental ADL tasks and the severity of cognitive impairment. The evaluation of ADLs involving the IoTs platform in an ecological setting allows accurate assessment and quantification of the patient's functional level.

Contact-free sensor signals as a new digital biomarker for cardiovascular disease: chances and challenges

Multiple sensor systems are used to monitor physiological parameters, activities of daily living and behaviour. Digital biomarkers can be extracted and used as indicators for health and disease. Signal acquisition is either by object sensors, wearable sensors, or contact-free sensors including cameras, pressure sensors, non-contact capacitively coupled electrocardiogram (cECG), radar, and passive infrared motion sensors. This review summarizes contemporary knowledge of the use of contact-free sensors for patients with cardiovascular disease and healthy subjects following the PRISMA declaration. Chances and challenges are discussed. Thirty-six publications were rated to be of medium (31) or high (5) relevance. Results are best for monitoring of heart rate and heart rate variability using cardiac vibration, facial camera, or cECG; for respiration using cardiac vibration, cECG, or camera; and for sleep using ballistocardiography. Early results from radar sensors to monitor vital signs are promising. Contact-free sensors are little invasive, well accepted and suitable for long-term monitoring in particular in patient's homes. A major problem are motion artefacts. Results from long-term use in larger patient cohorts are still lacking, but the technology is about to emerge the market and we can expect to see more clinical results in the near future.

A Simple Two-Dimensional Location Embedding for Passive Infrared Motion-Sensing based Home Monitoring Applications

Pervasive computing based home-monitoring has attracted increasing interest over the past years, especially regarding applications in the growing population of older adults. Applications include safety, monitoring chronic conditions like dementia, or providing preventive information about changes in health and behavior. Commonly used components of such systems are inexpensive and low-power passive infrared motion sensing units, usually placed in distinct locations of an older adult's apartment. To efficiently analyse the resulting data the majority of procedures expect the resulting sensor data to be encoded in a vector space. However, most common vector space encodings are based on orthogonal representations of the sensor locations and thus lead to loss of information as the sensors are placed in a 3D-space. In this work we introduce an embedding of sensor-locations in a 2D-space based on multidimensional scaling, without knowledge of the physical position of the sensors. We evaluate this embedding, using two different algorithms and compare it to commonly used baselines in different tasks. All evaluations are carried out on a real-world home-monitoring data-set.

Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods.

Methods: Data were collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community-dwelling adults over a period of 1 to 2 years. Physical activity in the apartment, toilet visits, refrigerator use, and entrance door openings were quantified using a commercially available passive infrared motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd., Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data were correlated with health-related data collected from the weekly visits of the seniors by health professionals, including information about physical, psychological, cognitive, and behavior status, health problems, diseases, medication, and medical diagnoses.

Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study; two participants had to stop their study participation because of severe health deterioration, whereas two participants died during the course of the study. A history of chronic disease was present in 12/24 seniors, including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes, and Parkinson's disease. In total, 242,232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of aging-relevant indicators and outcomes were performed. Several episodes of health deterioration, including heart failure worsening and heart rhythm disturbances, could be captured by sensor signals from different sources.

Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well-accepted, with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.

The Feasibility and Utility of a Personal Health Record for Persons With Dementia and Their Family Caregivers for Web-Based Care Coordination: Mixed Methods Study

Background

Managing the complex and long-term care needs of persons living with Alzheimer disease and related dementias (ADRD) can adversely impact the health of informal caregivers and their care recipients. Web-based personal health records (PHRs) are one way to potentially alleviate a caregiver’s burden by simplifying ADRD health care management

Objective

This study aimed to evaluate Personal Health Record for Persons with Dementia and Their Family Caregivers (PHR-ADRD), a free web-based information exchange tool, using a multiphase mixed methods approach.

Methods

Dementia caregivers (N=34) were surveyed for their well-being and perceptions of PHR-ADRD feasibility and utility at 6 and 12 months using close- and open-ended questions as well as a semistructured interview (n=8). Exploratory analyses compared participants’ characteristics as well as PHR-ADRD use and experiences based on overall favorability status.

Results

Feasibility and utility scores decreased over time, but a subset of participants indicated that the system was helpful. Quantitative comparisons could not explain why some participants indicated favorable, neutral, or unfavorable views of the system overall or had not engaged with PHR-ADRD. Qualitative findings suggested that technology literacy and primary care provider buy-in were barriers. Both qualitative and qualitative findings indicated that time constraints to learn and use the system affected most participants.

Conclusions

Development and dissemination of PHRs for family caregivers of persons with ADRD should aim to make systems user-friendly for persons with limited time and technological literacy. Establishing health care provider buy-in may be essential to the future success of any PHR system.

Big Data From Small Devices: The Future of Smartphones in Oncology

Technological advancements in the capabilities of modern smartphones offer tremendous potential to generate big data from small devices that could influence oncologists' decision-making. Here we describe the value of patient-generated health data (PGHD) that can be captured using mobile devices. We comment on the current use of smartphones in oncology clinical research and describe how smartphones will bring big data into the oncology clinic by enabling continuous patient monitoring, information sharing, and personalized clinical decision making in cancer care. Lastly, we describe practical considerations about how we can access and store PGHD in the future, describing how to harness the clinical value of PGHD and comment on the emerging applications for digital biomarkers captured by smartphones.

Drug-Related Problems Increase Healthcare Costs for People Living with Dementia

BACKGROUND

Drug-related problems (DRP) are common in the elderly population, especially in people living with dementia (PwD). DRP are associated with adverse outcomes that could result in increased costs.

OBJECTIVE

The objective of the study was to analyze the association between DRP and healthcare costs in PwD.

METHODS

The analysis was based on the cross-sectional data of 424 PwD. Compliance, adverse effects, and drug administration of prescribed and over-the-counter drugs taken were assessed. DRP were identified and classified by pharmacists using an adapted German version of "PIE-Doc®". Healthcare utilization was assessed retrospectively used to calculated costs from a public payer perspective using standardized unit costs. The associations between DRP and healthcare costs were analyzed using multiple linear regression models.

RESULTS

394 PwD (93%) had at least one DRP. An inappropriate drug choice was significantly associated with increased total costs (b = 2,718€; CI95% 1,448-3,988) due to significantly higher costs for hospitalization (b = 1,936€; 670-3,202) and for medications (b = 417€; 68-765). Problems with medication dosage and drug interactions were significantly associated with higher medication costs (b = 679€; 31-1,328; and b = 630€; 259-1,001, respectively).

CONCLUSIONS

DRP could significantly lead to adverse outcomes for PwD and healthcare payers, reflected by a higher hospitalization and costs, respectively. Further research is needed to clarify on interventions and approaches efficiently avoiding DRP and on the effect on patient-reported and economic outcomes.

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.

Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial

BACKGROUND

Most frail older persons are living at home, and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. People could give up their homes for the more reassuring environment of a medicalized living facility. We have developed a low-cost unobtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate older adults' follow-up in their own homes, and thus support independent living.

OBJECTIVE

The primary objective of this prospective open-label study is to evaluate the relevance of the automatic alerts generated by our artificial intelligence-driven monitoring solution as judged by the recipients: older adults, caregivers, and professional support workers. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events.

METHODS

The primary outcome will be evaluated for each successive 2-month follow-up period to estimate the progression of our learning algorithm performance over time. In total, 25 frail or disabled participants, aged 75 years and above and living alone in their own homes, will be enrolled for a 6-month follow-up period.

RESULTS

The first phase with 5 participants for a 4-month feasibility period has been completed and the expected completion date for the second phase of the study (20 participants for 6 months) is July 2020.

CONCLUSIONS

The originality of our real-life project lies in the choice of the primary outcome and in our user-centered evaluation. We will evaluate the relevance of the alerts and the algorithm performance over time according to the end users. The first-line recipients of the information are the older adults and their care partners rather than health care professionals. Despite the fast pace of electronic health devices development, few studies have addressed the specific everyday needs of older adults and their families.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03484156; https://clinicaltrials.gov/ct2/show/NCT03484156.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/14245.

Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review

BACKGROUND

Among areas that have challenged the progress of dementia care has been the assessment of change in symptoms over time. Digital biomarkers are defined as objective, quantifiable, physiological, and behavioral data that are collected and measured by means of digital devices, such as embedded environmental sensors or wearables. Digital biomarkers provide an alternative assessment approach, as they allow objective, ecologically valid, and long-term follow-up with continuous assessment. Despite the promise of a multitude of sensors and devices that can be applied, there are no agreed-upon standards for digital biomarkers, nor are there comprehensive evidence-based results for which digital biomarkers may be demonstrated to be most effective.

OBJECTIVE

In this review, we seek to answer the following questions: (1) What is the evidence for real-life, home-based use of technologies for early detection and follow-up of mild cognitive impairment (MCI) or dementia? And (2) What transformation might clinicians expect in their everyday practices?

METHODS

A systematic search was conducted in PubMed, Cochrane, and Scopus databases for papers published from inception to July 2018. We searched for studies examining the implementation of digital biomarker technologies for mild cognitive impairment or mild Alzheimer disease follow-up and detection in nonclinic, home-based settings. All studies that included the following were examined: community-dwelling older adults (aged 65 years or older); cognitively healthy participants or those presenting with cognitive decline, from subjective cognitive complaints to early Alzheimer disease; a focus on home-based evaluation for noninterventional follow-up; and remote diagnosis of cognitive deterioration.

RESULTS

An initial sample of 4811 English-language papers were retrieved. After screening and review, 26 studies were eligible for inclusion in the review. These studies ranged from 12 to 279 participants and lasted between 3 days to 3.6 years. Most common reasons for exclusion were as follows: inappropriate setting (eg, hospital setting), intervention (eg, drugs and rehabilitation), or population (eg, psychiatry and Parkinson disease). We summarized these studies into four groups, accounting for overlap and based on the proposed technological solutions, to extract relevant data: (1) data from dedicated embedded or passive sensors, (2) data from dedicated wearable sensors, (3) data from dedicated or purposive technological solutions (eg, games or surveys), and (4) data derived from use of nondedicated technological solutions (eg, computer mouse movements).

CONCLUSIONS

Few publications dealt with home-based, real-life evaluations. Most technologies were far removed from everyday life experiences and were not mature enough for use under nonoptimal or uncontrolled conditions. Evidence available from embedded passive sensors represents the most relatively mature research area, suggesting that some of these solutions could be proposed to larger populations in the coming decade. The clinical and research communities would benefit from increasing attention to these technologies going forward.