Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review

Using artificial intelligence for systematic review: the example of elicit

BACKGROUND

Artificial intelligence (AI) tools are increasingly being used to assist researchers with various research tasks, particularly in the systematic review process. Elicit is one such tool that can generate a summary of the question asked, setting it apart from other AI tools. The aim of this study is to determine whether AI-assisted research using Elicit adds value to the systematic review process compared to traditional screening methods.

METHODS

We compare the results from an umbrella review conducted independently of AI with the results of the AI-based searching using the same criteria. Elicit contribution was assessed based on three criteria: repeatability, reliability and accuracy. For repeatability the search process was repeated three times on Elicit (trial 1, trial 2, trial 3). For accuracy, articles obtained with Elicit were reviewed using the same inclusion criteria as the umbrella review. Reliability was assessed by comparing the number of publications with those without AI-based searches.

RESULTS

The repeatability test found 246,169 results and 172 results for the trials 1, 2, and 3 respectively. Concerning accuracy, 6 articles were included at the conclusion of the selection process. Regarding, revealed 3 common articles, 3 exclusively identified by Elicit and 17 exclusively identified by the AI-independent umbrella review search.

CONCLUSION

Our findings suggest that AI research assistants, like Elicit, can serve as valuable complementary tools for researchers when designing or writing systematic reviews. However, AI tools have several limitations and should be used with caution. When using AI tools, certain principles must be followed to maintain methodological rigour and integrity. Improving the performance of AI tools such as Elicit and contributing to the development of guidelines for their use during the systematic review process will enhance their effectiveness.

Activity and Behavioral Recognition Using Sensing Technology in Persons with Parkinson's Disease or Dementia: An Umbrella Review of the Literature

BACKGROUND

With a progressively aging global population, the prevalence of Parkinson's Disease and dementia will increase, thus multiplying the healthcare burden worldwide. Sensing technology can complement the current measures used for symptom management and monitoring. The aim of this umbrella review is to provide future researchers with a synthesis of the current methodologies and metrics of sensing technologies for the management and monitoring of activities and behavioral symptoms in older adults with neurodegenerative disease. This is of key importance when considering the rapid obsolescence of and potential for future implementation of these technologies into real-world healthcare settings.

METHODS

Seven medical and technical databases were searched for systematic reviews (2018-2024) that met our inclusion/exclusion criteria. Articles were screened independently using Rayyan. PRISMA guidelines, the Cochrane Handbook for Systematic Reviews, and the Johanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews were utilized for the assessment of bias, quality, and research synthesis. A narrative synthesis combines the study findings.

RESULTS

After screening 1458 articles, 9 systematic reviews were eligible for inclusion, synthesizing 402 primary studies. This umbrella review reveals that the use of sensing technologies for the observation and management of activities and behavioral symptoms is promising, however diversely applied, heterogenous in the methods used, and currently challenging to apply within clinical settings.

CONCLUSIONS

Human activity and behavioral recognition requires true interdisciplinary collaborations between engineering, data science, and healthcare domains. The standardization of metrics, ethical AI development, and a culture of research-friendly technology and support are the next crucial developments needed for this rising field.

Technological monitoring of motor parameters to assess multidimensional frailty of older people in the PRO-HOME project

An interconnected system employing Kinect Azure and Fitbit Sense for continuous and non-intrusive data collection was used in the PRO-HOME protected discharge program, aiming at monitoring functional and clinical parameters in hospitalized older patients at different risks of frailty. The present study shows the findings on 30 older patients included in the PRO-HOME project. The Fitbit Sense recorded the mean daily and hourly number of steps, mean daily walked distance, and time spent inactive. Moreover, Kinect infrared camera captured gait speed and daily mean latero-lateral (body sway) and antero-posterior oscillations (lean-in). Patients underwent a standard Comprehensive Geriatric Assessment (CGA) to compute the Multidimensional Prognostic Index (MPI), including basic and instrumental activities of daily living (ADL, IADL), cognition (Short Portable Mental Status Questionnaire, SPMSQ) and nutrition, risk of pressure sores (Exton-Smith Scale, ESS), comorbidity, number of drugs and cohabitation status. Significant correlations between the mean hourly number of steps and MPI (p = 0.022), IADL (p = 0.013), SPMSQ (p = 0.006), ESS (p = 0.009), and both mean and maximum automated gait speed (p = 0.046 and p = 0.048) were found. Automated gait speed was also correlated with mean walked distance per day (p = 0.007) and lean-in (p = 0.047). Domotic technological monitoring through Fitbit Sense and Kinect Azure provides information on multidimensional frailty, including mobility and cognitive and functional status, in older people.

Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol

Technological innovation has revolutionized healthcare, particularly in neurological rehabilitation, where it has been used to address chronic conditions. Smart home and building automation (SH&BA) technologies offer promising solutions for managing chronic disabilities associated with such conditions. This single group, pre-post longitudinal pilot study, part of the H2020 HosmartAI project, aims to explore the integration of smart home technologies into neurorehabilitation. Eighty subjects will be enrolled from IRCCS San Camillo Hospital (Venice, Italy) and will receive rehabilitation treatment through virtual reality (VR) and robotics devices for 15 h per day, 5 days a week for 3 weeks in the HosmartAI Room (HR), equipped with SH&BA devices measuring the environment. The study seeks to optimize patient outcomes and refine rehabilitation practices. Findings will be disseminated through peer-reviewed publications and scientific meetings, contributing to advancements in neurological rehabilitation and guiding future research.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Codevelopment and Deployment of a System for the Telemonitoring of Activities of Daily Living Among Older Adults Receiving Home Care Services: Protocol for an Action Design Research Study

BACKGROUND

Telemonitoring of activities of daily living (ADLs) offers significant potential for gaining a deeper insight into the home care needs of older adults experiencing cognitive decline, particularly those living alone. In 2016, our team and a health care institution in Montreal, Quebec, Canada, sought to test this technology to enhance the support provided by home care clinical teams for older adults residing alone and facing cognitive deficits. The Support for Seniors' Autonomy program (SAPA [Soutien à l'autonomie des personnes âgées]) project was initiated within this context, embracing an innovative research approach that combines action research and design science.

OBJECTIVE

This paper presents the research protocol for the SAPA project, with the aim of facilitating the replication of similar initiatives in the future. The primary objectives of the SAPA project were to (1) codevelop an ADL telemonitoring system aligned with the requirements of key stakeholders, (2) deploy the system in a real clinical environment to identify specific use cases, and (3) identify factors conducive to its sustained use in a real-world setting. Given the context of the SAPA project, the adoption of an action design research (ADR) approach was deemed crucial. ADR is a framework for crafting practical solutions to intricate problems encountered in a specific organizational context.

METHODS

This project consisted of 2 cycles of development (alpha and beta) that involved cyclical repetitions of stages 2 and 3 to develop a telemonitoring system for ADLs. Stakeholders, such as health care managers, clinicians, older adults, and their families, were included in each codevelopment cycle. Qualitative and quantitative data were collected throughout this project.

RESULTS

The first iterative cycle, the alpha cycle, took place from early 2016 to mid 2018. The first prototype of an ADL telemonitoring system was deployed in the homes of 4 individuals receiving home care services through a public health institution. The prototype was used to collect data about care recipients' ADL routines. Clinicians used the data to support their home care intervention plan, and the results are presented here. The prototype was successfully deployed and perceived as useful, although obstacles were encountered. Similarly, a second codevelopment cycle (beta cycle) took place in 3 public health institutions from late 2018 to late 2022. The telemonitoring system was installed in 31 care recipients' homes, and detailed results will be presented in future papers.

CONCLUSIONS

To our knowledge, this is the first reported ADR project in ADL telemonitoring research that includes 2 iterative cycles of codevelopment and deployment embedded in the real-world clinical settings of a public health system. We discuss the artifacts, generalization of learning, and dissemination generated by this protocol in the hope of providing a concrete and replicable example of research partnerships in the field of digital health in cognitive aging.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/52284.

Characteristics and Range of Reviews About Technologies for Aging in Place: Scoping Review of Reviews

BACKGROUND

It is a contemporary and global challenge that the increasing number of older people requiring care will surpass the available caregivers. Solutions are needed to help older people maintain their health, prevent disability, and delay or avoid dependency on others. Technology can enable older people to age in place while maintaining their dignity and quality of life. Literature reviews on this topic have become important tools for researchers, practitioners, policy makers, and decision makers who need to navigate and access the extensive available evidence. Due to the large number and diversity of existing reviews, there is a need for a review of reviews that provides an overview of the range and characteristics of the evidence on technology for aging in place.

OBJECTIVE

This study aimed to explore the characteristics and the range of evidence on technologies for aging in place by conducting a scoping review of reviews and presenting an evidence map that researchers, policy makers, and practitioners may use to identify gaps and reviews of interest.

METHODS

The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Literature searches were conducted in Web of Science, PubMed, and Scopus using a search string that consisted of the terms "older people" and "technology for ageing in place," with alternate terms using Boolean operators and truncation, adapted to the rules for each database.

RESULTS

A total of 5447 studies were screened, with 344 studies included after full-text screening. The number of reviews on this topic has increased dramatically over time, and the literature is scattered across a variety of journals. Vocabularies and approaches used to describe technology, populations, and problems are highly heterogeneous. We have identified 3 principal ways that reviews have dealt with populations, 5 strategies that the reviews draw on to conceptualize technology, and 4 principal types of problems that they have dealt with. These may be understood as methods that can inform future reviews on this topic. The relationships among populations, technologies, and problems studied in the reviews are presented in an evidence map that includes pertinent gaps.

CONCLUSIONS

Redundancies and unexploited synergies between bodies of evidence on technology for aging in place are highly likely. These results can be used to decrease this risk if they are used to inform the design of future reviews on this topic. There is a need for an examination of the current state of the art in knowledge on technology for aging in place in low- and middle-income countries, especially in Africa.

Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology

BACKGROUND

Outcome measures available for use in Alzheimer's disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated "digital biomarkers" (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology.

OBJECTIVES

The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort.

METHODS

Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the "ABC" assessment of AD-associated changes.

RESULTS

The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed.

DISCUSSION

This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

BACKGROUND

Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups.

OBJECTIVE

This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities.

METHODS

Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts.

RESULTS

This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome.

CONCLUSIONS

Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.

Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia

Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.

Passive Remote Monitoring and Aging in Place: A Scoping Review

Passive remote monitoring is a relatively new technology that may support older adults to age in place. However, current knowledge about the effectiveness of this technology in extending older adults' independence is lacking. Therefore, we conducted a scoping review of studies examining passive remote monitoring to systematically synthesize evidence about the technology's effectiveness as an intervention. Our initial search of Embase, CINAHL, PubMed, and Scopus databases identified 486 unique articles. Of these, 14 articles met our inclusion criteria. Results show that passive remote monitoring technologies are being used in innovative and diverse ways to support older adults aging in place and their caregivers. More high-quality research on this topic is needed.

Is research on 'smart living environments' based on unobtrusive technologies for older adults going in circles? Evidence from an umbrella review

CONTEXT AND AIMS

To enable ageing in place, innovative and integrative technologies such as smart living environments may be part of the solution. Despite extensive published literature reviews on this topic, the effectiveness of smart living environments in supporting ageing in place, and in particular involving unobtrusive technologies, remains unclear. The main objective of our umbrella review was to synthesize evidence on this topic.

METHODS

According to the PRIOR process, we included reviews from multiple databases that focused on unobtrusive technologies used to analyze and share information about older adults' behaviors and assessed the effectiveness of unobtrusive technologies to support ageing in place. Selection, extraction and quality appraisal were done independently by two reviewers.

RESULTS

By synthesizing 17 published reviews that covered 191 distinct primary studies, we found that smart living environments based on unobtrusive technologies had low to moderate effectiveness to support older adults to age in place. Effectiveness appears to be strongest in the recognition of activities of daily living. The results must, however, be interpreted in light of the low overall level of evidence, i.e., low methodological value of the primary studies and poor methodological quality of the literature reviews. Most reviews concluded that unobtrusive technologies are not mature enough for widespread adoption.

CONCLUSION

There is a necessity to support primary studies that can move beyond the proof-of-concept or pilot stages and expand scientific knowledge significantly on the topic. There is also an urgent need to publish high quality literature reviews to better support policy makers and funding agencies in the field of smart living environments.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Unobtrusive, in-home assessment of older adults' everyday activities and health events: associations with cognitive performance over a brief observation period

In-home assessment of everyday activities over many months to years may be useful in predicting cognitive decline in older adulthood. This study examined whether a comparatively brief data collection period (3 months) may yield similar diagnostic information. A total of 91 community-dwelling older adults without dementia underwent baseline neuropsychological testing and completed weekly computer-based surveys assessing health-related events/activities. A subset of participants wore fitness tracker watches assessing daily sleep and physical activity patterns, used a sensor-instrumented pillbox, and had their computer use frequency recorded on a daily basis. Similar patterns in computer use, sleep and medication use were noted in comparison to prior literature with more extensive data collection periods. Greater computer use and sleep, as well as self-reported pain and independence, were also linked to better cognition. These activities and symptoms may be useful correlates of cognitive function even when assessed over a relatively brief monitoring period.

Predictors of functional impairment and awareness in people with dementia, mild cognitive impairment and healthy older adults from a middle-income country

Objective

To investigate the demographic, clinical and cognitive correlates of functional capacity and its awareness in people with dementia (PwD; n = 104), mild cognitive impairment (PwMCI; n = 45) and controls (healthy older adults; n = 94) in a sample from a middle-income country.

Methods

Dementia and MCI were diagnosed, respectively, with DSM-IV and Petersen criteria. Performance in activities of daily living (ADL) at three different levels [basic (The Katz Index of Independence), instrumental (Lawton instrumental ADL scale) and advanced (Reuben's advanced ADL scale)], measured through self- and informant-report, as well as awareness (discrepancy between self- and informant-report), were compared between groups. Stepwise regression models explored predictors of ADL and their awareness.

Results

PwD showed impairment in all ADL levels, particularly when measured through informant-report. No differences were seen between controls and PwMCI regardless of measurement type. PwD differed in awareness of instrumental and basic, but not of advanced ADL, compared to controls. Age, gender, education and fluency were the most consistent predictors for ADL. Diagnosis was a significant predictor only for instrumental ADL. Awareness of basic ADL was predicted by memory, and awareness of instrumental ADL was predicted by general cognitive status, educational level, and diagnosis.

Conclusion

Results reinforce the presence of lack of awareness of ADL in PwD. Use of informant-reports and cognitive testing for fluency are suggested for the clinical assessment of ADL performance. Finally, assessment of instrumental ADL may be crucial for diagnostic purposes.

Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

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

Objectives

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

Results

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

Conclusion

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

Effectiveness of smart living environments to support older adults to age in place in their community: an umbrella review protocol

INTRODUCTION

Frailty is a vulnerable condition exposing older adults to incidental adverse health events that negatively impact their quality of life and increase health and social costs. Digital solutions may play a key role in addressing this global problem and in particular, smart living environments. Smart living environments involve a notion of measurement or collection of data via several sensors, capturing the person's behaviours in the home or the person's health status over a long period of time. It thus has great potential for home support for older adults. The objective of this umbrella review will be: (1) to document the effectiveness of smart living environments to support ageing in place for frail older adults and (2) among the reviews assessing the effectiveness of smart living environment, to gather evidence on what factors and strategies were identified as influencing the implementation process.

METHODS AND ANALYSIS

We will include systematic and scoping reviews of both quantitative and qualitative primary studies with or without meta-analysis focusing on assessing the effectiveness of interventions through smart living environments to support older adults in the community to age in place. The literature search will be done through the following biomedical, technological and sociological citation databases: MEDLINE, Embase, CINAHL, Web of Science and PsycINFO, and quality assessment of the reviews will be done thought AMSTAR2 checklist. The analysis of the results will be presented in narrative form.

ETHICS AND DISSEMINATION

Our review will rely exclusively on published data from secondary sources and will thus not involve any interactions with human subjects. The results will be presented at international conferences and publications.

PROSPERO REGISTRATION NUMBER

CRD42021249849.

Older Adults' Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Background and Objectives

The coronavirus disease 2019 (COVID-19) pandemic has limited older adults' access to in-person medical care, including screenings for cognitive and functional decline. Remote, technology-based tools have shown recent promise in assessing changes in older adults' daily activities and mood, which may serve as indicators of underlying health-related changes (e.g., cognitive decline). This study examined changes in older adults' driving, computer use, mood, and travel events prior to and following the COVID-19 emergency declaration using unobtrusive monitoring technologies and remote online surveys. As an exploratory aim, the impact of mild cognitive impairment (MCI) on these changes was assessed.

Research Design and Methods

Participants were 59 older adults (41 cognitively intact and 18 MCI) enrolled in a longitudinal aging study. Participants had their driving and computer use behaviors recorded over a 5-month period (75 days pre- and 76 days post-COVID emergency declaration) using unobtrusive technologies. Measures of mood, overnight guests, and frequency of overnight travel were also collected weekly via remote online survey.

Results

After adjusting for age, gender, and education, participants showed a significant decrease in daily driving distance, number of driving trips, highway driving, and nighttime driving, post-COVID-19 as compared to pre-COVID-19 (p < .001) based on generalized estimating equation models. Further, participants spent more time on the computer per day post-COVID-19 (p = .03). Participants endorsed increases in blue mood (p < .01) and loneliness (p < .001) and decreases in travel away from home and overnight visitors (p < .001) from pre- to post-COVID-19. Cognitive status did not impact these relationships.

Discussion and Implications

From pre- to post-COVID-19 emergency declaration, participants drove and traveled less, used their computer more, had fewer overnight visitors, and reported greater psychological distress. These results highlight the behavioral and psychological effects of stay-at-home orders on older adults who are cognitively intact and those with MCI.

AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults

In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment.

Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer's Disease: A Systematic Review

Alzheimer's disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer's patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer's disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer's. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer's disease.

Passively-Measured Routine Home Computer Activity and Application Use Can Detect Mild Cognitive Impairment and Correlate with Important Cognitive Functions in Older Adulthood

BACKGROUND

Computer use is a cognitively complex instrumental activity of daily living (IADL) that has been linked to cognitive functioning in older adulthood, yet little work has explored its capacity to detect incident mild cognitive impairment (MCI).

OBJECTIVE

To examine whether routine home computer use (general computer use as well as use of specific applications) could effectively discriminate between older adults with and without MCI, as well as explore associations between use of common computer applications and cognitive domains known to be important for IADL performance.

METHODS

A total of 60 community-dwelling older adults (39 cognitively healthy, 21 with MCI) completed a neuropsychological evaluation at study baseline and subsequently had their routine home computer use behaviors passively recorded for three months.

RESULTS

Compared to those with MCI, cognitively healthy participants spent more time using the computer, had a greater number of computer sessions, and had an earlier mean time of first daily computer session. They also spent more time using email and word processing applications, and used email, search, and word processing applications on a greater number of days. Better performance in several cognitive domains, but in particular memory and language, was associated with greater frequency of browser, word processing, search, and game application use.

CONCLUSION

Computer and application use are useful in identifying older adults with MCI. Longitudinal studies are needed to determine whether decreases in overall computer use and specific computer application use are predictors of incident cognitive decline.

Mild cognitive impairment history and current procedures in low- and middle-income countries: a brief review

Mild cognitive impairment (MCI) is a widely studied concept that has changed over time. Epidemiology, diagnosis, costs, prognostics, screening procedures, and categorization have been extensively discussed. However, unified guidelines are still not available, especially considering differences between low- and middle-income countries (LMIC) and high-income countries (HIC).

The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community

Introduction

Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities.

Methods

CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants' homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing.

Results

The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160-169 min), physical mobility (e.g., mean daily transitions between rooms = 96-155), sleep (e.g., mean nightly sleep duration = 6.3-7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15-45%) were collected across cohorts.

Conclusion

The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report

BACKGROUND

Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level.

OBJECTIVE

The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation.

METHODS

This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity.

RESULTS

Over time, the monitoring reports showed evolving trends in the care recipient's daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation.

CONCLUSIONS

Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse's questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

Functional parameters indicative of mild cognitive impairment: a systematic review using instrumented kinematic assessment

BACKGROUND

Patients with mild cognitive impairment (MCI) experience alterations of functional parameters, such as an impaired balance or gait. The current systematic review set out to investigate whether functional objective performance may predict a future risk of MCI; to compare functional objective parameters in patients with MCI and a control group; and to assess changes in these parameters after different physical activity interventions.

METHODS

Electronic databases, including PubMed, AMED, CINAHL, EMBASE, PEDro and Web of Science as well as grey literature databases, were searched from inception to February 2020. Cohort studies and Randomized Controlled Trials (RCTs) were included. The risk of bias of the included studies was assessed independently by reviewers using quality assessment checklists. The level of evidence per outcome was assessed using the GRADE criteria.

RESULTS

Seventeen studies met inclusion criteria including patients with MCI. Results from RCTs suggested that gait speed, gait variability and balance may be improved by different physical activity interventions. Cohort studies showed that slower gait speed, above all, under Dual Task (DT) conditions, was the main impaired parameter in patients with MCI in comparison with a Control Gorup. Furthermore, cohort studies suggested that gait variability could predict an incident MCI. Although most of included cohort studies reported low risk of bias, RCTs showed an unclear risk of bias.

CONCLUSIONS

Studies suggest that gait variability may predict an incident MCI. Moreover, different gait parameters, above all under DT conditions, could be impaired in patients with MCI. These parameters could be improved by some physical activity interventions. Although cohort studies reported low risk of bias, RCTs showed an unclear risk of bias and GRADE criteria showed a low level of evidence per outcome, so further studies are required to refute our findings.

PROSPERO

CRD42019119180.

Feasibility of In-Home Sensor Monitoring to Detect Mild Cognitive Impairment in Aging Military Veterans: Prospective Observational Study

BACKGROUND

Aging military veterans are an important and growing population who are at an elevated risk for developing mild cognitive impairment (MCI) and Alzheimer dementia, which emerge insidiously and progress gradually. Traditional clinic-based assessments are administered infrequently, making these visits less ideal to capture the earliest signals of cognitive and daily functioning decline in older adults.

OBJECTIVE

This study aimed to evaluate the feasibility of a novel ecologically valid assessment approach that integrates passive in-home and mobile technologies to assess instrumental activities of daily living (IADLs) that are not well captured by clinic-based assessment methods in an aging military veteran sample.

METHODS

Participants included 30 community-dwelling military veterans, classified as healthy controls (mean age 72.8, SD 4.9 years; n=15) or MCI (mean age 74.3, SD 6.0 years; n=15) using the Clinical Dementia Rating Scale. Participants were in relatively good health (mean modified Cumulative Illness Rating Scale score 23.1, SD 2.9) without evidence of depression (mean Geriatrics Depression Scale score 1.3, SD 1.6) or anxiety (mean generalized anxiety disorder questionnaire 1.3, SD 1.3) on self-report measures. Participants were clinically assessed at baseline and 12 months later with health and daily function questionnaires and neuropsychological testing. Daily computer use, medication taking, and physical activity and sleep data were collected via passive computer monitoring software, an instrumented pillbox, and a fitness tracker watch in participants' environments for 12 months between clinical study visits.

RESULTS

Enrollment began in October 2018 and continued until the study groups were filled in January 2019. A total of 201 people called to participate following public posting and focused mailings. Most common exclusionary criteria included nonveteran status 11.4% (23/201), living too far from the study site 9.4% (19/201), and having exclusionary health concerns 17.9% (36/201). Five people have withdrawn from the study: 2 with unanticipated health conditions, 2 living in a vacation home for more than half of the year, and 1 who saw no direct benefit from the research study. At baseline, MCI participants had lower Montreal Cognitive Assessment (P<.001) and higher Functional Activities Questionnaire (P=.04) scores than healthy controls. Over seven months, research personnel visited participants' homes a total of 73 times for technology maintenance. Technology maintenance visits were more prevalent for MCI participants (P=.04) than healthy controls.

CONCLUSIONS

Installation and longitudinal deployment of a passive in-home IADL monitoring platform with an older adult military veteran sample was feasible. Knowledge gained from this pilot study will be used to help develop acceptable and effective home-based assessment tools that can be used to passively monitor cognition and daily functioning in older adult samples.

An Ecologically Valid, Longitudinal, and Unbiased Assessment of Treatment Efficacy in Alzheimer Disease (the EVALUATE-AD Trial): Proof-of-Concept Study

Background

The current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests, and biomarkers. This methodology is limited by recall bias and recency effects in self-reporting and by assessments that are brief, episodic, and clinic based. Continuous monitoring of ecologically valid measures of cognition and daily functioning in the community may provide a more sensitive method to detect subtle, progressive changes in patients with cognitive impairment and dementia.

Objective

This study aimed to present an alternative trial approach using a home-based sensing and computing system to detect changes related to common treatments employed in Alzheimer disease (AD). This paper introduces an ongoing study that aims to determine the feasibility of capturing sensor-based data at home and to compare the sensor-based outcomes with conventional outcomes. We describe the methodology used in the assessment protocol and present preliminary results of feasibility measures and examples of data related to medication-taking behavior, activity levels, and sleep.

Methods

The EVALUATE-AD (Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer’s Disease) trial is a longitudinal naturalistic observational cohort study recruiting 30 patients and 30 spouse coresident care partners. Participants are monitored continuously using a home-based sensing and computing system for up to 24 months. Outcome measures of the automated system are compared with conventional clinical outcome measures in AD. Acceptance of the home system and protocol are assessed by rates of dropout and protocol adherence. After completion of the study monitoring period, a composite model using multiple functional outcome measures will be created that represents a behavioral-activity signature of initiating or discontinuing AD-related medications, such as cholinesterase inhibitors, memantine, or antidepressants.

Results

The home-based sensing and computing system has been well accepted by individuals with cognitive impairment and their care partners. Participants showed good adherence to the completion of a weekly web-based health survey. Daily activity, medication adherence, and total time in bed could be derived from algorithms using data from the sensing and computing system. The mean monitoring time for current participants was 14.6 months. Medication adherence, as measured with an electronic pillbox, was 77% for participants taking AD-related medications.

Conclusions

Continuous, home-based assessment provides a novel approach to test the impact of new or existing dementia treatments generating objective, clinically meaningful measures related to cognition and everyday functioning. Combining this approach with the current clinical trial methodology may ultimately reduce trial durations, sample size needs, and reliance on a clinic-based assessment.

International Registered Report Identifier (IRRID)

DERR1-10.2196/17603

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Older People’s Needs and Opportunities for Assistive Technologies

Older adults experience a disconnect between their needs and adoption of technologies that have potential to assist and to support more independent living. This paper reviewed research that links people’s needs with opportunities for assistive technologies. It searched 13 databases identifying 923 papers with 34 papers finally included for detailed analysis. The research papers identified needs in the fields of health, leisure, living, safety, communication, family relationship and social involvement. Amongst these, support for activities of daily living category was of most interest. In specific sub-categories, the next most reported need was assistive technology to support walking and mobility followed by smart cooking/kitchen technology and assistive technology for social contacts with family member/other people. The research aimed to inform a program of research into improving the adoption of technologies where they can ameliorate identified needs of older people.