Naturalistic assessment of everyday activities and prompting technologies in mild cognitive impairment

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.

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids

BACKGROUND

Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks.

AIM

This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone.

METHODS

Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data.

RESULTS

Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%).

CONCLUSIONS

Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.

Immersive robotic telepresence system to support a person with intellectual and motor disabilities perform a daily task: a case study

Purpose

In this proof-of-concept study, the authors assessed the feasibility of using a humanoid robot controlled remotely via an immersive telepresence system to support a person with intellectual and motor disabilities performing a daily task (i.e. setting a table for lunch).

Design/methodology/approach

The system involved a head-mounted display and two joysticks. A teleoperator was able to see through the video cameras of the robot and deliver the instructions verbally to the participant located in a different room. To assess the system, a baseline phase (A) was followed by an intervention (i.e. tele-operated support) phase (B) and a return to a baseline phase (A).

Findings

Data showed a marked increase in the average frequency of task steps correctly performed from baseline (M = 15%) to intervention (M = 93%). Accuracy reached 100% in the return to baseline.

Originality/value

These preliminary findings, along with qualitative feedback from users, suggest that an immersive telepresence system may be used to provide remote support to people with intellectual and motor disabilities.

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.

FreeSia: A Cyber-Physical System for Cognitive Assessment through Frequency-Domain Indoor Locomotion Analysis

Thanks to the seamless integration of sensing, networking, and artificial intelligence, cyber-physical systems promise to improve healthcare by increasing efficiency and reducing costs. Specifically, cyber-physical systems are being increasingly applied in smart-homes to support independent and healthy ageing. Due to the growing prevalence of noncommunicable diseases in the senior population, a key application in this domain is the detection of cognitive issues based on sensor data. In this paper, we propose a novel cyber-physical system for cognitive assessment in smart-homes. Cognitive evaluation relies on clinical indicators characterizing symptoms of dementia based on the individual’s movement patterns. However, recognizing these patterns in smart-homes is challenging, because movement is constrained by the home layout and obstacles. Since different abnormal patterns are characterized by undulatory-like trajectories, we conjecture that frequency-based locomotion features may more effectively capture these patterns with respect to traditional features in the spatio-temporal domain. Based on this intuition, we introduce novel feature extraction techniques, and adopt state of the art machine learning algorithms for short- and long-term cognitive evaluation. Our system includes a user-friendly interface that enables clinicians to inspect the data and predictions. Extensive experiments carried out with a real-world dataset acquired from both cognitively healthy seniors and people with dementia show the superiority of our frequency-based features. Moreover, further experiments with an ensemble method show that prediction accuracy can be enhanced by combining features in the frequency and time domains.

Machine Learning Applied to Datasets of Human Activity Recognition: Data Analysis in Health Care

BACKGROUND

In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments.

OBJECTIVE

This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities of the elderly or people with disabilities. Technological developments allow predictive analysis of daily life activities, contributing to the identification of patterns in advance in order to improve the quality of life of the elderly.

METHODS

The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive model capable of supporting the identification of activities in indoor environments. These datasets have some variation in terms of occupation and the number of daily living activities to be identified.

RESULTS

Twelve classifiers were implemented, among which the following stand out: Classification via Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee, Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying daily life activities are analyzed in the light of precision and recall quality metrics. For this specific experimentation, the Classification via Regression and OneR classifiers obtain the best results.

CONCLUSION

The efficiency of the predictive model based on classification is concluded, showing the results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher than 90% even when the datasets vary in occupation and number of activities.

Partnering a Compensatory Application with Activity-Aware Prompting to Improve Use in Individuals with Amnestic Mild Cognitive Impairment: A Randomized Controlled Pilot Clinical Trial

BACKGROUND

Compensatory aids can help mitigate the impact of progressive cognitive impairment on daily living.

OBJECTIVE

We evaluate whether the learning and sustained use of an Electronic Memory and Management Aid (EMMA) application can be augmented through a partnership with real-time, activity-aware transition-based prompting delivered by a smart home.

METHODS

Thirty-two adults who met criteria for amnestic mild cognitive impairment (aMCI) were randomized to learn to use the EMMA app on its own (N = 17) or when partnered with smart home prompting (N = 15). The four-week, five-session manualized EMMA training was conducted individually in participant homes by trained clinicians. Monthly questionnaires were completed by phone with trained personnel blind to study hypotheses. EMMA data metrics were collected continuously for four months. For the partnered condition, activity-aware prompting was on during training and post-training months 1 and 3, and off during post-training month 2.

RESULTS

The analyzed aMCI sample included 15 EMMA-only and 14 partnered. Compared to the EMMA-only condition, by week four of training, participants in the partnered condition were engaging with EMMA more times daily and using more basic and advanced features. These advantages were maintained throughout the post-training phase with less loss of EMMA app use over time. There was little differential impact of the intervention on self-report primary (everyday functioning, quality of life) and secondary (coping, satisfaction with life) outcomes.

CONCLUSION

Activity-aware prompting technology enhanced acquisition, habit formation and long-term use of a digital device by individuals with aMCI. (ClinicalTrials.gov NCT03453554).

Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

Technology options to help people with dementia or acquired cognitive impairment perform multistep daily tasks: a scoping review

Purpose

The purpose of this paper is to review studies that evaluated technology-based prompting systems for supporting participants with dementia or acquired cognitive impairment in their performance of multistep daily tasks.

Design/methodology/approach

A scoping review was conducted to identify eligible studies through a search of four electronic databases, that is, PubMed, PsycINFO, Web of Science and Institute of Electrical and Electronics Engineers.

Findings

The search, which covered the 2010–2020 period, led to the identification of 1,311 articles, 30 of which were included in the review. These articles evaluated six different types of prompting systems: context-aware, automatic computer prompting, context-aware, mediated computer prompting, teleoperated robot prompting, self-operated augmented reality prompting, self-operated computer or tablet prompting and time-based (preset) computer, tablet or smartphone prompting.

Originality/value

Technology-aided prompting to help people with dementia or acquired cognitive impairment perform relevant multistep daily tasks is considered increasingly important. This review provides a picture of the different prompting options available and of their level of readiness for application in daily contexts.

The goal-control model: An integrated neuropsychological framework to explain impaired performance of everyday activities

OBJECTIVE

This review describes the relatively small body of neuropsychological and cognitive research conducted over the past 100 years focused on theoretical models explaining the neurocognitive processes that support everyday functioning and the breakdown of functional abilities in the face of neurological damage or disease.

METHOD

The historical roots of the theories of everyday activities based on direct observation of behavior in neurology and diary reports of everyday errors in cognitive psychology are presented, followed by a review of the empirical findings and resulting theoretical conceptualizations from case studies and group studies of various clinical populations in neuropsychology.

RESULTS

We conclude with a new framework (the goal-control model) that integrates the most recent empirical findings in neuropsychology with mechanisms proposed by cognitive models.

CONCLUSIONS

The goal-control model offers empirically supported solutions to understanding and predicting functioning in the real world. This new model generates testable predictions for future research and provides guidance for clinical assessment and interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

I-CARE-An Interaction System for the Individual Activation of People with Dementia

I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CARE's recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers.

Exploring the acceptance of tablets usage for cognitive training among older people with cognitive impairments: A mixed-methods study

This study investigated the acceptance of tablets technology among cognitively impaired older adults from individual and contextual levels when used in cognitive training. A convergent parallel mixed-methods design, comprising a post-usage questionnaire survey and focus groups, was used for data collection. A number of 57 community-dwelling cognitively impaired older people in Hong Kong completed an eight-week, home-based cognitive training using tablets delivered by older volunteers. The acceptance of the tablet usage for cognitive training was evaluated using questionnaire survey. Focus groups were conducted with participants, volunteers, and social workers to explore their experiences of tablet usage for cognitive training. Results indicated that attitudes toward tablets and facilitating conditions were predictors of intention to use tablets at the individual level. Tablets were perceived as beneficial on cognition, enjoyment, learning, social relationships, and communication. Contextual level factors that can encourage tablets usage include capacity building, empowerment, supports from the organization, and trust.

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.

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.

Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-Encoders

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE's reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia.

Insight into memory and functional abilities in individuals with amnestic mild cognitive impairment

OBJECTIVE

Accurate insight into one's abilities facilitates engagement in rehabilitation and implementation of compensatory strategies. In this study, self-awareness, self-monitoring, and a new self-updating construct of insight were examined in amnestic mild cognitive impairment (aMCI).

METHOD

Individuals with aMCI and healthy older adults (HOAs) completed a list-learning task in a laboratory setting, and a naturalistic task of everyday functioning in a campus apartment along with other standardized neuropsychological tests. Participants made predictions about performance on the memory and functional tasks prior to task experience (self-awareness), immediately after task experience (self-monitoring), and after a delay (self-updating).

RESULTS

Individuals with aMCI performed more poorly than HOAs on the memory task and other neuropsychological tests but not the functional task. For both the memory and functional task, performance predictions and prediction accuracy measures revealed that the aMCI group exhibited intact self-awareness, self-monitoring, and self-updating. Prediction accuracy measures showed some association with an executive composite but not a memory composite.

DISCUSSION

Participants with aMCI demonstrated intact self-awareness, self-monitoring, and self-updating for a memory and functional task despite exhibiting poorer performance on neurocognitive tests compared to HOAs. These findings suggest that, even as memory in aMCI degrades, executive abilities may help sustain insight into difficulties, enabling adoption of cognitive strategies to support difficulties.

Differences in self-awareness of functional deficits between amnestic single- and multidomain mild cognitive impairment

INTRODUCTION

Prior research examining self-awareness of deficits in those with mild cognitive impairment (MCI) has been inconsistent, suggesting that preservation of insight at this disease stage may be conditional on the domain(s) examined as well as individual characteristics. The current study is the first to examine differences in objective performance and self-awareness of difficulties between older adults with amnestic single- (MCI-ASD) and multidomain MCI (MCI-AMD) across six instrumental activities of daily living (IADLs).

METHOD

Seventy-five individuals (M_age = 73.9 years, range = 55-88 years; 56% female) with MCI-ASD (n = 30) and MCI-AMD (n = 45) were recruited primarily from a hospital-based memory disorders clinic. Participants were administered self-report and objective measures assessing six functional domains: financial management, driving, telephone use, nutrition evaluation, grocery shopping, and medication management. Self-awareness discrepancy scores were calculated for each of these IADLs, and participants were classified as either "overestimating ability" or "accurately/underestimating ability."

RESULTS

Individuals with MCI-AMD performed significantly worse on objective measures of financial management, driving, and nutrition evaluation than those with MCI-ASD. Across MCI subtypes, participants were most likely to lack awareness of their difficulties in nutrition evaluation (31%), financial management (25%), and driving (23%) domains. Individuals with MCI-AMD were significantly more likely than those with MCI-ASD to overestimate performance on driving and telephone use domains.

CONCLUSION

Individuals with MCI-AMD are more likely than those with MCI-ASD to have impairment in their everyday function and to lack awareness into their IADL difficulties. When possible, clinicians should obtain objective measures in combination with detailed informant reports of functional abilities in order to evaluate capacity to independently engage in various daily activities. Finally, level of self-awareness varies across IADL domains, providing further evidence that insight is not a unitary construct.

Prompting Technology and Persons With Dementia: The Significance of Context and Communication

Background and Objectives

Smart home auto-prompting has the potential to increase the functional independence of persons with dementia (PWDs) and decrease caregiver burden as instrumental activities of daily living (IADLs) are completed at home. To improve prompting technologies, we sought to inductively understand how PWDs responded to auto-prompting while performing IADL tasks.

Research Design and Methods

Fifteen PWDs completed eight IADLs in a smart home testbed and received a hierarchy of verbal auto-prompts (indirect, direct, multimodal) as needed for task completion. Two researchers viewed archived videos and recorded the observed behaviors of the PWDs and their reflections watching the PWDs. Using qualitative descriptive methods, an interdisciplinary analytic team reviewed transcripts and organized data into themes using content analysis.

Results

Context and Communication emerged as the major themes, suggesting that positive user experiences will require auto-prompting systems to account for a multitude of contextual factors (individual and environmental) such as level of cognitive impairment, previous exposure to task, and familiarity of environment. Communicating with another human rather than an automated prompting system may be important if individuals begin to exhibit signs of stress while completing activities.

Discussion and Implications

Additional work is needed to create auto-prompting systems that provide specific, personalized, and flexible prompts. Holistic conceptualization of "successful task completion" is needed and a positive end-user experience will be key to utility. Such systems will benefit from including positive reinforcement, training, and exploration of how, and whether, direct human involvement can be minimized during the provision of in-home care.

Convergent and concurrent validity of a report- versus performance-based evaluation of everyday functioning in the diagnosis of cognitive disorders in a geriatric population

ABSTRACTBackground:Several methods have been developed to evaluate activities of daily living (ADLs) in mild cognitive impairment (MCI) and mild dementia. This study evaluated the convergent and concurrent validity between (1) two report-based methods (the advanced (a)- and instrumental (i)-ADL tools) and (2) a performance-based method (the Naturalistic Action Test (NAT)) to check if their ability to differentiate between cognitively healthy comparisons (HCs), persons with MCI, and persons with mild Alzheimer's disease (AD) are comparable to each other.

METHOD

This was a cross-sectional study, undertaken in a geriatric day hospital. The participants comprised community-dwelling HCs (n = 21, median age 78.0 years, 61.9% female), MCI (n = 20, median age 79.5 years, 55.0% female), and AD (n = 20, median age 80.0 years, 85.0% female) adults. A diagnostic procedure for neurocognitive disorders was employed. In addition, the a- and i-ADL tools and the NAT were administered separately by blinded raters.

RESULTS

The NAT and both the a- and i-ADL tools showed significant differences between HCs, MCI, and AD participants. Convergent validity showed moderate to strong significant correlations between the NAT, and a- and i-ADL tools (range -0.583 to -0.663; p < 0.01). Concurrent validity showed that the NAT (AUC 0.809-1.000) and the a- and i-ADL tools (AUC 0.739-0.964) presented comparable discriminatory accuracy (p = 0.0588).

CONCLUSIONS

In contrast to prior studies comparing report-based and performance-based methods of assessing ADL, this study indicates that the NAT and the a- and i-ADL tools have strong convergent and concurrent validity, and appear to have similar discriminatory power in differentiating between HCs, MCI, and AD.

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

The aging of the world population is accompanied by a substantial increase in neurodegenerative disorders, such as dementia. Early detection of mild cognitive impairment (MCI), a clinical diagnostic that comes with an increased chance to develop dementias, could be an essential condition for promoting quality of life and independent living, as it would provide a critical window for the implementation of early pharmacological and nonpharmacological interventions. This systematic review aims to investigate the current state of knowledge on the effectiveness of smart home sensors technologies for the early detection of MCI through the monitoring of everyday life activities. This approach offers many advantages, including the continuous measurement of functional abilities in ecological environments. A systematic search of publications in MEDLINE, EMBASE, and CINAHL, before November 2017, was conducted. Seventeen studies were included in this review. Thirteen studies were based on real-life monitoring, with several sensors installed in participants' actual homes, and four studies included scenario-based assessments, in which participants had to complete various tasks in a research lab apartment. In real-life monitoring, the most used indicators of MCI were walking speed and activity/motion in the house. In scenario-based assessment, time of completion, quality of activity completion, number of errors, amount of assistance needed, and task-irrelevant behaviors during the performance of everyday activities predicted MCI in participants. Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.

Automatic assessment of functional health decline in older adults based on smart home data

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of >2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy.

Embedded Online Questionnaire Measures Are Sensitive to Identifying Mild Cognitive Impairment

BACKGROUND/AIMS

Early changes in cognitively demanding daily activities occur between normal cognition and the development of mild cognitive impairment (MCI). These real-world functional changes as early signals of cognitive change form a prime target for meaningful early detection of dementia. We examined whether passive aspects of responding to a remotely monitored weekly online questionnaire discriminated between older adults with and without MCI.

METHODS

Participants were 83 independent, community-dwelling older adults enrolled in a longitudinal study of in-home monitoring technologies, which included completion of a short weekly online questionnaire of health and life events.

RESULTS

In longitudinal analyses, time to complete the online questionnaire decreased over 1 year in both MCI and cognitively intact participants (P<0.01). MCI and intact participants did not differ in the time of day they submitted their questionnaires initially; however, over the course of 1 year MCI participants began to submit their questionnaires progressively later in the day and they needed greater assistance from staff as compared with intact participants (P<0.05). The online questionnaire performance measures were significantly correlated to conventional cognitive test scores (P<0.05) across the spectrum of normal cognition to MCI.

CONCLUSIONS

Ambiently assessed, passive performance measures embedded within an online questionnaire are able to discriminate between normal cognition and MCI. Remote monitoring of cognitively demanding routine daily activities is a promising approach for ecologically valid real-world cognitive assessment.

Mild Cognitive Impairment and Changes in Everyday Function Over Time: The Importance of Evaluating Both Speed and Accuracy

OBJECTIVES

Research estimates that a significant percentage of individuals with mild cognitive impairment (MCI) experience functional difficulties. In addition to reduced accuracy on measures of everyday function, cross-sectional research has demonstrated that speed of performing instrumental activities of daily living (IADLs) is slowed in individuals with MCI. The present study investigated whether baseline and longitudinal changes in speed and accuracy of IADL performance differed between persons with MCI and cognitively normal peers.

DESIGN

Linear mixed models were used to estimate the group differences in longitudinal performance on measures of IADLs.

SETTING

Assessments were conducted at university and medical research centers.

PARTICIPANTS

The sample consisted of 80 participants with MCI and 80 control participants who were enrolled in the Alzheimer's Disease Research Center's Measuring Independent Living in the Elderly Study.

MEASUREMENTS

Instrumental activities of daily living speed and accuracy were directly assessed using selected domains of the Financial Capacity Instrument, the Timed IADL assessment, and driving-related assessments (Useful Field of View, Road Sign Test).

RESULTS

Individuals with MCI performed worse on speed and accuracy measures of IADLs in comparison to cognitively normal peers and demonstrated significantly steeper rates of decline over three years in either speed or accuracy in all domains assessed.

CONCLUSION

Both speed and accuracy of performance on measures of IADL are valuable indices for early detection of functional change in MCI. The performance pattern may reflect a trade-off between speed and accuracy that can guide clinical recommendations for maintaining patient independence.

Development of a Proxy-Free Objective Assessment Tool of Instrumental Activities of Daily Living in Mild Cognitive Impairment Using Smart Home Technologies

BACKGROUND

The assessment of activities of daily living (ADL) is essential for dementia diagnostics. Even in mild cognitive impairment (MCI), subtle deficits in instrumental ADL (IADL) may occur and signal a higher risk of conversion to dementia. Thus, sensitive and reliable ADL assessment tools are important. Smart homes equipped with sensor technology and video cameras may provide a proxy-free assessment tool for the detection of IADL deficits.

OBJECTIVE

The aim of this paper is to investigate the potential of a smart home environment for the assessment of IADL in MCI.

METHOD

The smart home consisted of a two-room flat equipped with activity sensors and video cameras. Participants with either MCI or healthy controls (HC) had to solve a standardized set of six tasks, e.g., meal preparation, telephone use, and finding objects in the flat.

RESULTS

MCI participants needed more time (1384 versus 938 seconds, p <  0.001) and scored less total points (48 versus 57 points, p <  0.001) while solving the tasks than HC. Analyzing the subtasks, intergroup differences were observed for making a phone call, operating the television, and retrieving objects. MCI participants showed more searching and task-irrelevant behavior than HC. Task performance was correlated with cognitive status and IADL questionnaires but not with participants' age.

CONCLUSION

This pilot study showed that smart home technologies offer the chance for an objective and ecologically valid assessment of IADL. It can be analyzed not only whether a task is successfully completed but also how it is completed. Future studies should concentrate on the development of automated detection of IADL deficits.

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

Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

A Genetic Algorithm Approach to Motion Sensor Placement in Smart Environments

Smart environments and ubiquitous computing technologies hold great promise for a wide range of real world applications. The medical community is particularly interested in high quality measurement of activities of daily living. With accurate computer modeling of older adults, decision support tools may be built to assist care providers. One aspect of effectively deploying these technologies is determining where the sensors should be placed in the home to effectively support these end goals. This work introduces and evaluates a set of approaches for generating sensor layouts in the home. These approaches range from the gold standard of human intuition-based placement to more advanced search algorithms, including Hill Climbing and Genetic Algorithms. The generated layouts are evaluated based on their ability to detect activities while minimizing the number of needed sensors. Sensor-rich environments can provide valuable insights about adults as they go about their lives. These sensors, once in place, provide information on daily behavior that can facilitate an aging-in-place approach to health care.

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

OBJECTIVE

In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective.

METHODS

A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level.

RESULTS

We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.

Age trajectories of everyday cognition in African American and White older adults under prompted and unprompted conditions

We investigated how race and verbal prompting interacted with age to predict age trajectories on a performance-based measure of everyday cognition. African American (n = 727) and White (n = 2052) older adults from the ACTIVE clinical trial were given the Observed Tasks of Daily Living (OTDL; a performance-based measure of medication management/finances/telephone use) at baseline and 1-, 2-, 3-, 5-, and 10-year follow-ups. When participants said "I don't know" or did not respond, they received a standardised verbal prompt, which served only as a cue to initiate the first step. At each occasion, unprompted (sum of items correct without prompting) and prompted (sum of correct prompted and unprompted items) scores were derived for each participant. Mixed effects models for change were used to determine the age trajectories of OTDL performance by race. When not prompted, African Americans demonstrated more rapid decline in OTDL performance than Whites, especially after age 80. When prompted, both groups had improved performance and evinced shallower decline, although African Americans continued to demonstrate a slightly more rapid decline. Simple prompting attenuated age-related changes of African Americans and Whites on a measure of everyday cognition. Prompting may be especially helpful for older African Americans.

Specific cognitive functions and depressive symptoms as predictors of activities of daily living in older adults with heterogeneous cognitive backgrounds

Cognitive functioning influences activities of daily living (ADL). However, studies reporting the association between ADL and neuropsychological performance show inconsistent results regarding what specific cognitive domains are related to each specific functional domains. Additionally, whether depressive symptoms are associated with a worse functional performance in older adults is still under explored. We investigated if specific cognitive domains and depressive symptoms would affect different aspects of ADL. Participants were 274 older adults (96 normal aging participants, 85 patients with mild cognitive impairment, and 93 patients probable with mild Alzheimer's disease dementia) with low formal education (∼4 years). Measures of ADL included three complexity levels: Self-care, Instrumental-Domestic, and Instrumental-Complex. The specific cognitive functions were evaluated through a factorial strategy resulting in four cognitive domains: Executive Functions, Language/Semantic Memory, Episodic Memory, and Visuospatial Abilities. The Geriatric Depression Scale measured depressive symptoms. Multiple linear regression analysis showed executive functions and episodic memory as significant predictors of Instrumental-Domestic ADL, and executive functions, episodic memory and language/semantic memory as predictors of Instrumental-Complex ADL (22 and 28% of explained variance, respectively). Ordinal regression analysis showed the influence of specific cognitive functions and depressive symptoms on each one of the instrumental ADL. We observed a heterogeneous pattern of association with explained variance ranging from 22 to 38%. Different instrumental ADL had specific cognitive predictors and depressive symptoms were predictive of ADL involving social contact. Our results suggest a specific pattern of influence depending on the specific instrumental daily living activity.

Informing Understandings of Mild Cognitive Impairment for Older Adults: Implications From a Scoping Review

The development of effective interventions for mild cognitive impairment (MCI) in older adults has been limited by extensive variability in the conceptualization and definition of MCI, its subtypes, and relevant diagnostic criteria within the neurocultural, pharmaceutical, and gerontological communities. A scoping review was conducted to explore the conceptual development of MCI and identify the resulting ethical, political, and technological implications for the care of older adults with MCI. A comprehensive search was conducted between January and April 2013 to identify English-language peer-reviewed articles published between 1999 and 2013. Our analysis revealed that the MCI conceptual debate remains unresolved, the response to ethical issues is contentious, the policy response is limited, and one-dimensional and technological interventions are scarce. Reflections on the conceptual, ethical, and policy responses in conjunction with the identification of the needs of older adults diagnosed with MCI highlight significant opportunities for technological interventions to effectively reposition MCI in the aging care discourse.

Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy

Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

The Potential Utility of Eye Movements in the Detection and Characterization of Everyday Functional Difficulties in Mild Cognitive Impairment

Mild cognitive impairment (MCI) refers to the intermediate period between the typical cognitive decline of normal aging and more severe decline associated with dementia, and it is associated with greater risk for progression to dementia. Research has suggested that functional abilities are compromised in MCI, but the degree of impairment and underlying mechanisms remain poorly understood. The development of sensitive measures to assess subtle functional decline poses a major challenge for characterizing functional limitations in MCI. Eye-tracking methodology has been used to describe visual processes in everyday, naturalistic action among healthy older adults as well as several case studies of severely impaired individuals, and it has successfully differentiated healthy older adults from those with MCI on specific visual tasks. These studies highlight the promise of eye-tracking technology as a method to characterize subtle functional decline in MCI. However, to date no studies have examined visual behaviors during completion of naturalistic tasks in MCI. This review describes the current understanding of functional ability in MCI, summarizes findings of eye-tracking studies in healthy individuals, severe impairment, and MCI, and presents future research directions to aid with early identification and prevention of functional decline in disorders of aging.

Translating cognitive and everyday activity deficits into cognitive interventions in mild dementia and mild cognitive impairment

OBJECTIVE

Mild dementia is marked by deficits in cognition and everyday activities. However, few studies have translated findings from both areas of functioning into effective cognitive rehabilitation. The purpose of this review was to critically evaluate the existing literature on the type and success of interventions and on their extent of use of cognitive theory. Given the limited evidence base in this population, further insights were obtained from studies on mild cognitive impairment (MCI), which involves fewer cognitive and everyday functioning problems than dementia.

METHODS

From the literature searches, 11 studies on mild dementia and three studies on MCI were obtained. Studies were only included if the interventions either targeted instrumental activities of daily living or activities of daily living directly or as an outcome measure or if the interventions focused on real-life aspects not captured in the standardised daily activities. For inclusion, patients needed a diagnosis of dementia or MCI, and Mini-Mental State Examination scores had to be above 17 for mild dementia.

RESULTS

The majority of interventions indicated improved everyday activity performance in early dementia and MCI. Focusing on individual, as opposed to global, daily activities appeared to be an important determinant of intervention success in mild dementia but not in MCI. However, few attempts had been made to develop interventions grounded in evidence-based models.

CONCLUSIONS

This review highlights the need for further translation of the understanding of cognitive and everyday activity deficits into successful interventions for daily activities in MCI and early dementia. Hence, research is first required to link individual activities with cognitive domains.

Assessment of functional change and cognitive correlates in the progression from healthy cognitive aging to dementia

OBJECTIVE

There is currently limited understanding of the course of change in everyday functioning that occurs with normal aging and dementia. To better characterize the nature of this change, we evaluated the types of errors made by participants as they performed everyday tasks in a naturalistic environment.

METHOD

Participants included cognitively healthy younger adults (YA; n = 55) and older adults (OA; n = 88), and individuals with mild cognitive impairment (MCI: n = 55) and dementia (n = 18). Participants performed 8 scripted everyday activities (e.g., filling a medication dispenser) while under direct observation in a campus apartment. Task performances were coded for the following errors: inefficient actions, omissions, substitutions, and irrelevant actions.

RESULTS

Performance accuracy decreased with age and level of cognitive impairment. Relative to the YAs, the OA group exhibited more inefficient actions which were linked to performance on neuropsychological measures of executive functioning. Relative to the OAs, the MCI group committed significantly more omission errors which were strongly linked to performance on memory measures. All error types were significantly more prominent in individuals with dementia. Omission errors uniquely predicted everyday functional status as measured by both informant-report and a performance-based measure.

CONCLUSIONS

These findings suggest that in the progression from healthy aging to MCI, everyday task difficulties may evolve from task inefficiencies to task omission errors, leading to inaccuracies in task completion that are recognized by knowledgeable informants. Continued decline in cognitive functioning then leads to more substantial everyday errors, which compromise ability to live independently.