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The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014-2019; 2019-2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them. Responding to the needs of researchers within the CCNA teams, a unique sample of 1173 deeply phenotyped patients with various forms of dementia was accrued and studied over eight years (COMPASS-ND). In the second phase of funding (2019-2024), a national dementia prevention program (CAN-THUMBS UP) was set up. In a short time, this prevention program became a member of the World Wide FINGERS prevention consortium. In this article, the challenges, successes, and impacts of CCNA in Canada and internationally are discussed. Short-term deliverables have occurred, along with considerable promise of impacts in the longer term. The creation of synergy, networking, capacity building, engagement of people with lived experience, and economies of scale have contributed to the considerable success of CCNA by all measures. CCNA is evidence that an organized "centrally-organized" approach to dementia research can catalyze important progress nationally and yield significant and measurable results., Competing Interests: Declaration of conflicting interestsDrs. Krista Lanctot, Manuel Montero-Odasso, Sylvie Belleville, and Julie Robillard, are Editorial Board Members of this journal but were not involved in the peer-review process of this article nor had access to any information regarding its peer-review.

Background: In Canada, more than 60% of persons living with dementia reside in their own homes, and over 25% rely heavily on their care partners (ie, family members or friends) for assistance with daily activities such as personal hygiene, eating, and walking. Assistive technology (AT) is a key dementia management strategy, helping to maintain health and social support in home and community settings. AT comprises assistive products and services required for safe and effective use. Persons living with dementia and their care partners often require multiple types of AT to maintain their needs, dignity, and autonomy. AT for dementia management is rapidly developing with abundant scientific literature, which can present a challenge to efficiently navigate and extract insights for policy and personal decision-making., Objective: This scoping review aims to synthesize review-level evidence from published scientific literature on AT to support dementia management for persons living with dementia and their care partners in their homes and communities. Research gaps in knowledge and areas for further investigation into the use and access of AT will be identified. This review will provide an overview of AT types and characteristics and chart the outcomes and conclusions in review-level evidence., Methods: This review will follow the Joanna Briggs Institute's framework for conducting scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 6 electronic databases will be searched. Articles will be screened according to the "Population-Concept-Context (PCC)" framework for eligible studies. Population includes persons living with dementia, their care partners, and health care professionals (eg, therapists or others who recommend AT). Concept includes AT and self-help devices of many types. Context includes homes and communities. A data charting template will guide data extraction, charting, and summarization. A descriptive numerical summary and an overview of the findings will be presented. Data, such as (1) article information (eg, author and year), (2) article characteristics (eg, review type), (3) AT types and characteristics, (4) setting and population characteristics, and (5) key review outcomes and conclusions, will be extracted., Results: A total of 10,978 unique citations were identified across the 6 electronic databases. This review is in the full-text screening stage, which is expected to be completed by October 2024., Conclusions: This review will provide a comprehensive understanding and documentation of the published scientific literature on AT to support dementia management. Findings from this review are expected to provide evidence-based insights on the complexities of AT types, uses, availability, and access. The author group's diverse national and international perspectives may contribute to knowledge exchange and influence standards to improve the daily function, safety, and well-being of persons living with dementia., Trial Registration: Open Science Framework DKSM9; https://osf.io/dksm9., International Registered Report Identifier (irrid): PRR1-10.2196/57036., (©Chaitali Desai, Erica Dove, Jarshini Nanthakumar, Emilia Main, Heather Colquhoun, Arlene Astell, Alex Mihailidis, Natasha Layton, Amer M Burhan, Brian Chan, Rosalie H Wang. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.11.2024.)

Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment., Objective: This review aims to evaluate existing studies on hand function evaluation using smartphones., Methods: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions., Results: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation., Conclusions: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses., (©Yan Fu, Yuxin Zhang, Bing Ye, Jessica Babineau, Yan Zhao, Zhengke Gao, Alex Mihailidis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.09.2024.)

Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments. In this study, we present a comprehensive overview of the StairNet initiative and key research to date. First, we summarize the development of our large-scale data set with over 515,000 manually labeled images. We then provide a summary and detailed comparison of the performances achieved with different algorithms (i.e., 2D and 3D CNN, hybrid CNN and LSTM, and ViT networks), training methods (i.e., supervised learning with and without temporal data, and semi-supervised learning with unlabeled images), and deployment methods (i.e., mobile and embedded computing), using the StairNet data set. Finally, we discuss the challenges and future directions. To date, our StairNet models have consistently achieved high classification accuracy (i.e., up to 98.8%) with different designs, offering trade-offs between model accuracy and size. When deployed on mobile devices with GPU and NPU accelerators, our deep learning models achieved inference speeds up to 2.8 ms. In comparison, when deployed on our custom-designed CPU-powered smart glasses, our models yielded slower inference speeds of 1.5 s, presenting a trade-off between human-centered design and performance. Overall, the results of numerous experiments presented herein provide consistent evidence that StairNet can be an effective platform to develop and study new deep learning models for visual perception of human-robot walking environments, with an emphasis on stair recognition. This research aims to support the development of next-generation vision-based control systems for robotic prosthetic legs, exoskeletons, and other mobility assistive technologies., (© 2024. The Author(s).)

Background: Studies have shown that mobile apps have the potential to serve as nonpharmacological interventions for dementia care, improving the quality of life of people living with dementia and their informal caregivers. However, little is known about the needs for and privacy aspects of these mobile apps in dementia care., Objective: This review seeks to understand the landscape of existing mobile apps in dementia care for people living with dementia and their caregivers with respect to app features, usability testing, privacy, and security., Methods: ACM Digital Library, Cochrane Central Register of Controlled Trials, Compendex, Embase, Inspec, Ovid MEDLINE, PsycINFO, and Scopus were searched. Studies were included if they included people with dementia living in the community, their informal caregivers, or both; focused on apps in dementia care using smartphones or tablet computers; and covered usability evaluation of the app. Records were independently screened, and 2 reviewers extracted the data. The Centre for Evidence-Based Medicine critical appraisal tool and Mixed Methods Appraisal Tool were used to assess the risk of bias in the included studies. Thematic synthesis was used, and the findings were summarized and tabulated based on each research aim., Results: Overall, 44 studies were included in this review, with 39 (89%) published after 2015. In total, 50 apps were included in the study, with more apps developed for people living with dementia as end users compared with caregivers. Most studies (27/44, 61%) used tablet computers. The most common app feature was cognitive stimulation. This review presented 9 app usability themes: user interface, physical considerations, screen size, interaction challenges, meeting user needs, lack of self-awareness of app needs, stigma, technological inexperience, and technical support. In total, 5 methods (questionnaires, interviews, observations, logging, and focus groups) were used to evaluate usability. There was little focus on the privacy and security aspects, including data transfer and protection, of mobile apps for people living with dementia., Conclusions: The limitations of this review include 1 reviewer conducting the full-text screening, its restriction to studies published in English, and the exclusion of apps that lacked empirical usability testing. As a result, there may be an incomplete representation of the available apps in the field of dementia care. However, this review highlights significant concerns related to the usability, privacy, and security of existing mobile apps for people living with dementia and their caregivers. The findings of this review provide a valuable framework to guide app developers and researchers in the areas of privacy policy development, app development strategies, and the importance of conducting thorough usability testing for their apps. By considering these factors, future work in this field can be advanced to enhance the quality and effectiveness of dementia care apps., Trial Registration: PROSPERO CRD42020216141; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216141., International Registered Report Identifier (irrid): RR2-10.1159/000514838., (©Bing Ye, Charlene H Chu, Sayeh Bayat, Jessica Babineau, Tuck-Voon How, Alex Mihailidis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.10.2023.)

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data., Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00313-8., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© Korean Society of Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: Anger dyscontrol is a common issue after traumatic brain injury (TBI). With the growth of wearable physiological sensors, there is new potential to facilitate the rehabilitation of such anger in the context of daily life. This potential, however, depends on how well physiological markers can distinguish changing emotional states and for such markers to generalize to real-world settings. Our study explores how wearable photoplethysmography (PPG), one of the most widely available physiological sensors, could be used detect anger within a heterogeneous population., Methods: This study collected the TRIEP (Toronto Rehabilitation Institute Emotion-Physiology) dataset, which comprised of 32 individuals (10 TBI), exposed to a variety of elicitation material (film, pictures, self-statements, personal recall), over two day sessions. This complex dataset allowed for exploration into how the emotion-PPG relationship varied over changes in individuals, endogenous/exogenous drivers of emotion, and day-to-day differences. A multi-stage analysis was conducted looking at: (1) times-series visual clustering, (2) discriminative time-interval features of anger, and (3) out-of-sample anger classification., Results: Characteristics of PPG are largely dominated by inter-subject (between individuals) differences first, then intra-subject (day-to-day) changes, before differentiation into emotion. Both TBI and non-TBI individuals showed evidence of linear separable features that could differentiate anger from non-anger classes within time-interval analysis. However, what is more challenging is that these separable features for anger have various degrees of stability across individuals and days., Conclusion: This work highlights how there are contextual, non-stationary challenges to the emotion-physiology relationship that must be accounted for before emotion regulation technology can perform in real-world scenarios. It also affirms the need for a larger breadth of emotional sampling when building classification models., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Powered wheelchairs promote participation for people with mobility limitations. For older adults with cognitive impairment, existing training methods may not address learning needs, leading to difficulty with powered wheelchair skills. Error-minimized training, facilitated by shared control technology, may provide learning opportunities more suited to this population., Objective: The objective of this study was to evaluate the feasibility of an error-minimized approach to powered wheelchair skills training using shared control in residential care. Feasibility indicators were hypothesized a priori to be feasible for use in a definitive RCT., Methods: A 2 × 2 factorial RCT compared an error-minimized powered wheelchair skills training program (Co-pilot) to a control intervention at two doses (6 sessions vs. 12 sessions). Data were collected on the feasibility of study processes (e.g., recruitment), resources (e.g., participant time), management (e.g., technology reliability), and training outcomes (e.g., adverse events, clinical outcomes)., Results: Twenty-five older adults with cognitive impairment participated in the study. Technical issues were encountered in 14.5% of training sessions. Participants receiving 6 sessions of training adhered better to the treatment than those receiving 12 sessions. All participants learned the skills required for PWC use with minor errors, regardless of the training method or dose. Co-pilot participants and trainers reported feelings of safety and training benefits with the use of shared control technology., Conclusions: Individuals with mild to moderate cognitive impairment are able to learn the skills required to drive a powered wheelchair in as few as six training sessions. Further evaluation of the Co-pilot training program is required.IMPLICATIONS FOR REHABILITATIONShared control teleoperation technology may be used to augment learning in older adults with cognitive impairments.Evaluation of the feasibility of use of novel rehabilitation technologies is critical prior to engaging in large-scale clinical research.Individuals with cognitive impairment are able to learn the required skills for operation of a powered wheelchair.

Objectives: Automatic speech and language assessment methods (SLAMs) can help clinicians assess speech and language impairments associated with dementia in older adults. The basis of any automatic SLAMs is a machine learning (ML) classifier that is trained on participants' speech and language. However, language tasks, recording media, and modalities impact the performance of ML classifiers. Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment., Methodology: Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment., Results: Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features., Conclusion: This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants' speech; (2) Collect participants' voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. Our proposed methodology will help future researchers to investigate the impacts of different factors on the performance of ML classifiers for assessing dementia., (© 2023. The Author(s).)

Background and Objectives: The prospect of automated vehicles (AVs) has generated excitement among the public and the research community about their potential to sustain the safe driving of people with dementia. However, no study to date has assessed the views of people with dementia on whether AVs may address their driving challenges., Research Design and Methods: This mixed-methods study included two phases, completed by nine people with dementia. Phase I included questionnaires and individual semistructured interviews on attitudes toward using different types of AVs (i.e., partially or fully automated). Interpretative phenomenological analysis was used to assess participants' underlying reasons for and against AV use. The participants' identified reasons against AV use informed the focus group discussions in Phase II, where participants were asked to reflect on potential means of overcoming their hesitancies regarding AV use., Results: The results showed that people with dementia might place higher levels of trust in fully automated compared to partially automated AVs. In addition, while people with dementia expressed multiple incentives to use AVs (e.g., regaining personal freedom), they also had hesitations about AV use. These hesitancies were based on their perceptions about AVs (e.g., cost), their own abilities (i.e., potential challenges operating an AV), and driving conditions (i.e., risk of driving in adverse weather conditions)., Discussion and Implications: The findings of this study can help promote the research community's appreciation and understanding of the significant potential of AVs for people with dementia while elucidating the potential barriers of AV use by people with dementia., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns., Purpose: In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia., Methods: We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies., Results: We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach., Conclusions: This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community., (© 2023. The Author(s).)

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL ('Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People'), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies., Competing Interests: Conflicts of Interest/Competing interestsThe authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript., (© The Author(s) 2022.)

Background and Objectives: Driving cessation is a complex challenge with significant emotional and health implications for people with dementia, which also affects their family care partners. Automated vehicles (AVs) could potentially be used to delay driving cessation and its adverse consequences for people with dementia and their care partners. Yet, no study to date has investigated whether care partners consider AVs to be potentially useful for people with dementia., Research Design and Methods: This mixed-methods study assessed the views of 20 former or current family care partners of people with dementia on AV use by people with dementia. Specifically, questionnaires and semistructured interviews were used to examine care partners' acceptance of AV use by people with dementia and their views about the potential usefulness of AVs for people with dementia., Results: The results demonstrated that care partners identified possible benefits of AV use by people with dementia such as their anticipated higher social participation. However, care partners also voiced major concerns around AV use by people with dementia and reported significantly lower levels of trust in and perceived safety of AVs if used by the person with dementia in their care compared to themselves. Care partners' concerns about AV use by people with dementia included concerns around the driving of people with dementia that AVs are not designed to address; concerns that are specific to AVs but are not relevant to the nonautomated driving of people with dementia; and concerns that arise from existing challenges around the nonautomated driving of people with dementia but may be exacerbated by AV use., Discussion and Implications: Findings from this study can inform future designs of AVs that are more accessible and useful for people with dementia., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: Powered wheelchair use promotes participation in individuals with limited mobility, however training is required for safe and effective use. There is limited evidence on the task demands of powered wheelchair use to inform an evidence-based skills training programme., Objective: To conduct a systematic exploration of the task demands of indoor powered wheelchair use to identify frequently used skills, abilities, and knowledge., Methods: We used a two-phased think aloud process to conduct a task analysis of powered wheelchair use with experienced powered wheelchair users ( n  = 5) and expert clinicians ( n  = 5). Participants completed seven indoor driving tasks while speaking aloud (concurrent think aloud) and subsequently engaged in a structured qualitative interview to discuss skills, abilities, and knowledge used across each of the seven tasks (retrospective think aloud). We used directed content analysis to map the skills and abilities to the ICF framework and conventional content analysis to develop thematic areas of knowledge used while operating a powered wheelchair., Results: One-hundred and ten (110) distinct skills and abilities were identified and mapped to the ICF; 80 in the Body Structures and Functions domain, and 30 in the Activities and Participation domain. Approximately 50% of skills and abilities were mental functions. Four thematic knowledge domains were identified: knowledge of self, environment, wheelchair, and task., Conclusion: Powered wheelchair use is complex and requires a variety of skills and abilities from all areas of human functioning, in addition to a wide range of knowledge. Training programmes should address a range of areas of skill development.IMPLICATIONS FOR REHABILITATIONPowered wheelchair use is a complex skill; training should develop skills from all.Domains of the ICF, including mental and physical functions.A range of knowledge is used while operating a powered wheelchair; training programs.Should include the development and application of necessary knowledge.Clinicians may consider a range of factors when assessing suitability for powered.Wheelchair user, however should acknowledge that while the range of skills idenotified.May be useful, they may not be critical for success in powered wheelchair use.

Computer vision can be used in robotic exoskeleton control to improve transitions between different locomotion modes through the prediction of future environmental states. Here we present the development of a large-scale automated stair recognition system powered by convolutional neural networks to recognize indoor and outdoor real-world stair environments. Building on the ExoNet database- the largest and most diverse open-source dataset of wearable camera images of walking environments-we designed a new computer vision dataset, called StairNet, specifically for stair recognition with over 515,000 images. We then developed and optimized an efficient deep learning model for automatic feature engineering and image classification. Our system was able to accurately predict complex stair environments with 98.4% classification accuracy. These promising results present an opportunity to increase the autonomy and safety of human-exoskeleton locomotion for real-world community mobility. Future work will explore the mobile deployment of our automated stair recognition system for onboard real-time inference.

Purpose: The iWalk study showed that 10-meter walk test (10mWT) and 6-minute walk test (6MWT) administration post-stroke increased among physical therapists (PTs) following introduction of a toolkit comprising an educational guide, mobile app, and video. We describe the use of theory guiding toolkit development and a process evaluation., Materials and Methods: We used the knowledge-to-action framework to identify research steps; and a guideline implementability framework, self-efficacy theory, and the transtheoretical model to design and evaluate the toolkit and implementation process (three learning sessions). In a before-and-after study, 37 of the 49 participating PTs completed online questionnaires to evaluate engagement with learning sessions, and rate self-efficacy to perform recommended practices pre- and post-intervention. Thirty-three PTs and 7 professional leaders participated in post-intervention focus groups and interviews, respectively., Results: All sites conducted learning sessions; attendance was 50-78%. Self-efficacy ratings for recommended practices increased and were significant for the 10mWT ( p  ≤ 0.004). Qualitative findings highlighted that theory-based toolkit features and implementation strategies likely facilitated engagement with toolkit components, contributing to observed improvements in PTs' knowledge, attitudes, skill, self-efficacy, and clinical practice., Conclusions: The approach may help to inform toolkit development to advance other rehabilitation practices of similar complexity.Implications for RehabilitationToolkits are an emerging knowledge translation intervention used to support widespread implementation of clinical practice guideline recommendations.Although experts recommend using theory to inform the development of knowledge translation interventions, there is little guidance on a suitable approach.This study describes an approach to using theories, models and frameworks to design a toolkit and implementation strategy, and a process evaluation of toolkit implementation.Theory-based features of the toolkit and implementation strategy may have facilitated toolkit implementation and practice change to increase clinical measurement and interpretation of walking speed and distance in adults post-stroke.

Background: Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation., Objective: This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation., Methods: We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question., Results: A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures., Conclusions: This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored., (©Xiaoyi Wang, Yan Fu, Bing Ye, Jessica Babineau, Yong Ding, Alex Mihailidis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.06.2022.)

Objectives: The COVID-19 pandemic is having a major impact on the lives of everyone, but in particular on the health and well-being of older people. It has also disrupted the way that individuals access services and interact with one another, and physical distancing and "Stay at Home" orders have seen digital interaction become a necessity. While these restrictions have highlighted the importance of technology in everyday life, little is known about how older adults have responded to this change. Methods: Two surveys, one in 2019 and another in 2020 collected data on a combined total of 1923 older adults aged 65 years and older in Canada. These looked at how older adults think about and use technology, with the 2020 survey additionally questioning how COVID-19 has impacted their use and attitudes towards technology. Results: While older adults feel more isolated in 2020, many feel positive about the benefits of technology and have increased technology use during the pandemic to support their health, wellness, and communication needs. Discussion: The results highlight the potential of technology for supporting older adults in various aspects of healthy aging. While these results point to the opportunities afforded by technology, challenges remain, such as how social and economic factors influence technology uptake., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland−Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings.

Introduction: Behavioral and psychological symptoms of dementia (BPSD) signal distress or unmet needs and present a risk to people with dementia and their caregivers. Variability in the expression of these symptoms is a barrier to the performance of digital biomarkers. The aim of this study was to use wearable multimodal sensors to develop personalized machine learning models capable of detecting individual patterns of BPSD., Methods: Older adults with dementia and BPSD (n = 17) on a dementia care unit wore a wristband during waking hours for up to 8 weeks. The wristband captured motion (accelerometer) and physiological indicators (blood volume pulse, electrodermal activity, and skin temperature). Agitation or aggression events were tracked, and research staff reviewed videos to precisely annotate the sensor data. Personalized machine learning models were developed using 1-minute intervals and classifying the presence of behavioral symptoms, and behavioral symptoms by type (motor agitation, verbal aggression, or physical aggression)., Results: Behavioral events were rare, representing 3.4% of the total data. Personalized models classified behavioral symptoms with a median area under the receiver operating curve (AUC) of 0.87 (range 0.64-0.95). The relative importance of the different sensor features to the predictive models varied both by individual and behavior type., Discussion: Patterns of sensor data associated with BPSD are highly individualized, and future studies of the digital phenotyping of these behaviors would benefit from personalization., Competing Interests: Outside of this work, AI has received research grants from the Canadian Institutes of Health Research; AGE‐WELL NCE; Province of Ontario; and the Centre for Aging + Brain Health Innovation and an Academic Scholars Award, Department of Psychiatry, University of Toronto. SS has received postdoctoral funding from the University Health Network, AGE‐WELL NCE, and Royal Bank of Canada. KN has received grants from the Social Sciences and Humanities Research Council. AW received a research grant from the Nursing Research Interest Group, Ryerson University. LSM is president of the not‐for‐profit Canadian Gerontological Nursing Association. SSK has received a research grant from the Alzehimer's Society and a honorarium from the International Conference on Gerontechnology, Hong Kong. BY and AM have no disclosures., (© 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Background: Caregiving is highly stressful and is associated with poor mental and physical health. Various technologies, including mobile and eHealth apps, have been developed to address caregiver needs. However, there is still a paucity of research examining the technology perceptions of informal caregivers, especially from the perspectives of sex, gender, and diversity., Objective: To address the research gap and inform the development of future caregiving technologies, this study aims to examine how family caregivers perceive using technology to assist with their caregiving routines; identify the sex, gender, and diversity factors that shape these perceptions; and understand how these perceptions and needs are reflected within the current technology development process., Methods: Semistructured interviews were conducted with 16 informal caregivers of individuals with a range of chronic medical conditions and 8 technology researchers involved in caregiving technology projects., Results: Three main themes with subthemes were developed. The first main theme is that caregivers see a need for technology in their lives, and it comprises the following 3 subthemes: caregiving is a challenging endeavor, technology is multifaceted, and caregiver preferences facilitate technology use. The second main theme is that relationships play a vital role in mediating technology uptake, and it comprises the following 2 subthemes: the caregiver-care recipient dynamic shapes technology perceptions and caregivers rely on external sources for technology information. Finally, the third main theme is that barriers are present in the use and adoption of technology, and it comprises the following 2 subthemes: technology may not be compatible with personal values and abilities and technology that is not tailored toward caregivers lacks adoption., Conclusions: The findings highlight the multifaceted role that technology can play in aiding caregiving while drawing attention to the perceived drawbacks of these technologies among caregivers. The inclusion of technology researchers in this study provides a more holistic understanding of technologies in caregiving from their initial development to their eventual uptake by caregivers., (©Chen Xiong, Andrea D'Souza, Graziella El-Khechen-Richandi, Alex Mihailidis, Jill I Cameron, Arlene Astell, Emily Nalder, Angela Colantonio. Originally published in JMIR Formative Research (https://formative.jmir.org), 28.01.2022.)

Introduction: An active lifestyle may protect older adults from cognitive decline. Yet, due to the complex nature of outdoor environments, many people living with dementia experience decreased access to outdoor activities. In this context, conceptualizing and measuring outdoor mobility is of great significance. Using the global positioning system (GPS) provides an avenue for capturing the multi-dimensional nature of outdoor mobility. The objective of this study is to develop a comprehensive framework for comparing outdoor mobility patterns of cognitively intact older adults and older adults with dementia using passively collected GPS data., Methods: A total of 7 people with dementia (PwD) and 8 cognitively intact controls (CTLs), aged 65 years or older, carried a GPS device when travelling outside their homes for 4 weeks. We applied a framework incorporating 12 GPS-based indicators to capture spatial, temporal, and semantic dimensions of outdoor mobility., Results: Despite a small sample size, the application of our mobility framework identified several significant differences between the 2 groups. We found that PwD participated in more medical-related (Cliff's Delta = 0.71, 95% CI: 0.34-1) and fewer sport-related (Cliff's Delta = -0.78, 95% CI: -1 to -0.32) activities compared to the cognitively intact CTLs. Our results also suggested that longer duration of daily walking time (Cliff's Delta = 0.71, 95% CI: 0.148-1) and longer outdoor activities at night, after 8 p.m. (Hedges' g = 1.42, 95% CI: 0.85-1.09), are associated with cognitively intact individuals., Conclusion: Based on the proposed framework incorporating 12 GPS-based indicators, we were able to identify several differences in outdoor mobility in PwD compared with cognitively intact CTLs., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Background: Mental health issues are on the rise which may impede university students' abilities to perform daily functions and interact with other community members. The objectives of the current study are to explore (1) the characteristics of university students' mental health and engagement in activities, (2) how students use their smartphones to support their mental health and engagement in activities, (3) student preferences for important features and functions of a smartphone application (app) that promote engagement in activities and (4) student perspectives about what data an app should collect as indicators of change in their mental health and engagement in activities., Methods: We designed a pilot study and an online questionnaire with open and closed-ended questions to collect data exploring the association between student mental health and engagement in activities. The questionnaire included four sections: demographics, mental health and activity status and management, general smartphone use, and smartphone use to support mental health and engagement in activities. The data were analysed using descriptive statistics., Results: A total of 56 participants were recruited to complete the online survey, with an average completion rate of 77% ( n = 43). The majority of participants were 24 years of age or older ( n = 34, 65.4%), and less than half were between the ages of 18 and 23 ( n = 18, 34.6%). The results of participants' engagement in self-care, productivity and leisure/play activities are reported. As well, participants' use of smartphones to support their mental health is described., Conclusions: This study provides a greater understanding of what features and functions to include and what data to collect when developing a novel app to support students' mental health and engagement in activities. Moreover, it clarifies the bidirectional relationship between mental health changes and self-care engagement, productivity/work and leisure/play domains., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Ballistocardiogram (BCG) is an emerging tool with the potential to monitor heart failure (HF) patients. A close association of the weight to the BCG as an intermediate signal source requires a careful design, where events such as saturation of the weight signal can result in the loss of the BCG. This work closely examined the factors around the weight while load cells placed under each support of a bed collected the BCG (e.g., body weight, distribution over the four supports of the bed). Following the calibration of weights based on the location of the polls, the study examined the ratios of loads in head-foot and lateral directions. The head-foot ratio was also correlated to the height. Twelve non-obese HF patients were recruited, and the weight and BCG were appropriately measured, where the average error of the weight measurements was 0.45 ± 0.30%. The mean ratio of the loads between head to foot sensors was 3.2 ± 0.7 with a maximum ratio of 4.5, showing that the head-ward sensors supported greater body weight. The ratio of the loads between the right to left sensors was 1.2 ± 0.1. The height and the head-to-foot ratio had an inverse correlation (r = 0.52). Based on the analysis, the head-ward sensors should have a higher capacity of up to three times that of the foot-ward sensors to prevent any signal saturation. Mobility issues were observed in some subjects, attributing to the lateral imbalance. These novel findings based on the end-users (i.e., HF population) may allow better allocation of conditioning resources to obtain the BCG (e.g., optimally adjusted sensitivity).

Onset and offset detection of electromyography (EMG) data is an important step in respiratory muscle coordination assessment. Impaired respiratory coordination can indicate breathing disorders and lung diseases. In this paper, we present an algorithm for onset and offset timing detection of real-world EMG signals from respiratory muscles, which are contaminated with electrocardiogram (ECG) artifacts. The algorithm is based on the Energy Operator signal, has a low computational cost, and includes a filtering procedure to remove ECG artifacts from EMG. Analysis of EMG signals from 2 respiratory muscles of 5 participants' data shows high agreement between the algorithm and manual method with a mean difference between two methods of 0.0407 seconds.

The COVID-19 pandemic presents an unprecedented situation in which physical distancing and "stay at home" orders have increased the pressures for social isolation. Critically, certain demographic factors have been linked to increased feelings of isolation and loneliness. These at-risk groups for social isolation may be disproportionately affected by the changes and restrictions that have been implemented to prevent viral spread. In our analysis, we sought to evaluate if perceived feelings of social isolation, during the COVID-19 pandemic, was related to demographic and technology-related psychographic characteristics. Older adults across Canada were surveyed about their demographic background, their feelings concerning confidence and proficiency in technology use, and how frequently they have felt isolated during the pandemic. In total 927 responses from Canadians over 65 years old, of varying demographic characteristics were collected. Our data shows that many older adults are feeling isolated "Often" or "Some of the time" in 2020, regardless of most demographic factors that have been previously associated with increased isolation risk. However, feelings of proficiency in using technology was an important factor affecting feelings of isolation. Given that technology proficiency is a modifiable factor, and remained significant after adjustment for demographic factors, future efforts to reduce social isolation should consider training programs for older adults to improve technology confidence, especially in an increasingly digital world., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Horst, Sixsmith, Simeonov and Mihailidis.)

Objective: To quantify the effect of multiple sclerosis (MS) on spatiotemporal gait characteristics accounting for disability severity and fall classification., Data Sources: MEDLINE (1946-August 2018), Allied and Complementary Medicine Database (1985-2018 August), and PsycINFO (1806-August 2018) were searched for terms on MS and gait., Study Selection: Dual independent screening was conducted to identify observational, cross-sectional studies that compared adults with MS grouped according to Expanded Disability Status Scale (EDSS) level or fall history, reported on spatiotemporal gait characteristics, and were published in English. The search retrieved 5891 results, of which 12 studies satisfied the inclusion criteria., Data Extraction: Two authors worked independently to extract and verify data on publication details, study methodology, participant characteristics, gait outcomes, conclusions, and limitations. Risk of bias was assessed using the QualSyst critical appraisal tool. A random-effects meta-regression and meta-analysis were conducted on pooled data., Data Synthesis: All studies received quality ratings of very good to excellent and collectively examined 1513 individuals with MS. With every 1-point increase in EDSS, significant changes (P<.05) were observed in gait speed (-0.12 m/s; 95% confidence interval (CI), 0.08-0.15), step length (-0.04 m; 95% CI, 0.03-0.05), step time (+0.04 seconds; 95% CI, 0.02-0.06), step time variability (+0.009 seconds; 95% CI, 0.003-0.016), stride time (+0.08 seconds; 95% CI, 0.03-0.12), cadence (-4.4 steps per minute; 95% CI, 2.3-6.4), stance phase duration (+0.8% gait cycle; 95% CI, 0.1-1.5), and double support time (+3.5% gait cycle; 95% CI, 1.5-5.4). Recent fallers exhibited an 18% (95% CI, 13%-23%) reduction in gait speed compared with nonfallers (P<.001)., Conclusions: This review provides the most accurate reference values to-date that can be used to assess the effectiveness of MS gait training programs and therapeutic techniques for individuals who differ on disability severity and fall classification. Some gait adaptations could be part of adopting a more cautious gait strategy and should be factored into the design of future interventions., (Copyright © 2021 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Alzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods., Methods: We followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD., Results: The F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96., Conclusion: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

Introduction: People with dementia (PWD) often become disoriented, which increases their risk of getting lost. This article explores the extent to which we can predict future whereabouts of PWD by learning from their past mobility patterns using Global Positioning System (GPS) tracking devices., Methods: Seven older adults with dementia and eight healthy older adults completed 8 weeks of GPS data collection. We computed the probability that an appropriate algorithm can correctly predict the participant's future destinations using spatial and temporal patterns in each participant's GPS trajectories., Results: Relying on both spatial and temporal patterns, our results suggest that a 4-week record of mobility patterns displays 95% potential predictability across the dementia group, which is significantly higher than 92% potential predictability among the controls, t(13) = -3.39, P  < .01, d = -1.75. That is, we can hope to be able to predict destinations of PWD about 95% of the time and destinations of controls about 92% of the time., Discussions: Our findings on predictability of mobility patterns among PWD offer new perspectives on predictive mobility models that can be used to locate missing persons with dementia., Competing Interests: The authors have no conflicts of interest to declare., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Background: The rapid development of technology such as sensors and artificial intelligence in recent years enables monitoring frailty criteria to assess frailty early and accurately from a remote location such as a home. However, research shows technologies being abandoned or rejected by users due to a lack of compatibility and consumer involvement in selecting their assistive technology devices. This study aims to understand older adults' perceptions and preferences of technologies that can potentially assess frailty at home., Methods: This study collected qualitative data through focus group meetings with 15 participants ages 65 and older. Researchers asked participants questions to achieve the goal of understanding their attitudes on the technologies. These questions include (1) the concerns or barriers of installing and using the presented technology in daily life at home, (2) the reasons participants like or dislike a particular technology, (3) what makes a specific technology more acceptable, and (4) participants' preferences in choosing technologies. Data were transcribed, coded and categorized, and finally synthesized to understand the attitudes towards presented technologies., Results: Three focus group sessions were conducted with five participants in each session. In the findings, the attitudes and perspectives of participants on the technologies for assessing frailty were categorized into four themes: (A) general attitude towards using the technologies, (B) conditions for accepting certain technologies, (C) existing living habits or patterns related to using the technologies, and (D) constructive suggestions related to the technologies., Conclusions: Participants generally had positive attitudes towards allowing the technologies to be installed and used at their homes. They would accept some technologies if used under certain conditions. However, questions and concerns remain, such as concerns about privacy, functionality, and aesthetics. The study also found that older adults' living habits or patterns could affect the design and use of technology. Lastly, many valuable suggestions have been made by participants. These perspectives and insights can help improve the design and adoption of home-based frailty assessment technologies among older adults.

Automated vehicles are anticipated to have benefits for older adults in maintaining their mobility and autonomy. These anticipated benefits can only be realized if this technology is accepted and thus used by older adults. However, it remains unclear how certain factors affect older adults' acceptance of automated vehicles. This study investigated the extent to which older adults' acceptance of fully automated vehicles are affected by exposure to automated vehicle technology (pre- vs. post-exposure), driving style (manual style relative to automated style), driving conditions (clear, rain, traffic), and age. Thirty-six older adults (M = 73.25, SD = 5.96) completed non-automated (manual) and fully automated driving scenarios under different driving conditions in a high-fidelity driving simulator. The fully automated driving scenarios were designed to be reliably driven by the system in a conservative driving style. Driving conditions included clear daytime, rain, and high-traffic. Pre- and post-exposure to the simulated fully automated driving experience, participants rated their comfort level with fully automated vehicles (FAVs). Additionally, after each driving condition, participants answered a validated questionnaire on their acceptance of the simulated fully automated experience for each respective driving condition. Age and driving style were found to have a significant effect on older adults' acceptance of FAVs, with older age and greater dissimilarity of an individual's manual driving style from the FAV's driving style being associated with lower acceptance. The results suggest that if reliability of fully automated vehicles is ultimately ensured and is demonstrated to the older adults, their acceptance of fully automated vehicles is generally high, particularly if the FAV is operated in a style similar to their own., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Dementia drastically impacts the quality of life (QOL) of both people living with dementia (PLwD) and their family caregivers. As dementia progresses and care needs escalate, the likelihood of institutionalization is increased, which is counter to the wishes of the majority of older adults and their family members. Dementia care apps can provide critical support and have the potential to improve the QOL of both PLwD and their family caregivers and reduce perceived caregivers' burden. However, there is a lack of understanding of the needs of both PLwD and their family caregivers related to dementia care apps. There is also a gap in understanding the privacy concerns in relation to the apps among older adults with dementia and their caregivers. As such, the main aims of this systematic review are to understand the landscape of dementia mobile apps targeting PLwD and their caregivers with respect to the features of the apps, usability testing, and the privacy and security aspects of the app from the perspective of both app developers/researchers and the end users (PLwD and family caregivers who provide care of PLwD). Extensive databases, including ACM Digital Library, Cochrane Central Register of Controlled Trials, Compendex, Embase, Inspec, Ovid MEDLINE(R) Daily, Proquest Dissertations and These Global, PsycINFO, and Scopus, have been searched. All searches are from the inception of the databases. All peer-reviewed studies and articles written in the English language are included. Two reviewers will independently screen and select the studies with the involvement of a third reviewer for disagreements. Data will be abstracted using a custom data extraction form that is made based on the research questions. Critical Appraisal Skills Programme (CASP) checklists will be used to assess the study quality. As the first review of its kind, the findings from this review will provide valuable insights related to the needs of the dementia care apps for both PLwD and their family caregivers. The review will be relevant to health providers who are interested in using technologies to promote the independence of PLwD and reduce the stress experienced from caregivers of PLwD. The review will also serve as a guide to app developers and researchers to design usable and acceptable apps. In addition, the review will provide critical knowledge of the privacy and security features of the app to reveal the valid concerns from the end users and thus help with the uptake and adoption of the dementia care apps., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Understanding older adults' relationships with their environments and the way this relationship evolves over time have been increasingly acknowledged in gerontological research. This relationship is often measured in terms of life-space, defined as the spatial area through which a person moves within a specific period of time. Life-space is traditionally reported using questionnaires or travel diaries and is, thus, subject to inaccuracies. More recently, studies are using a global positioning system to accurately measure life-space. Although life-space provides useful insights into older adults' relationships with their environment, it does not capture the inherent complexities of environmental exposures. In the fields of travel behaviour and health geography, a substantial amount of research has looked at people's spatial behaviour using the notion of "Activity Space," allowing for increasing sophistication in understanding older adults' experience of their environment. This manuscript discusses developments and directions for extending the life-space framework in environmental gerontology by drawing on the advancements in the activity space framework., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Introduction: Wearable robots controlled using electromyography could motivate greater use of the affected upper extremity after stroke and enable bimanual activities of daily living to be completed independently., Methods: We have developed a myoelectric untethered robotic glove (My-HERO) that provides five-finger extension and grip assistance., Results: The myoelectric controller detected the grip and release intents of the 9 participants after stroke with 84.7% accuracy. While using My-HERO, all 9 participants performed better on the Fugl-Meyer Assessment-Hand (8.4 point increase, scale out of 14, p < 0.01) and the Chedoke Arm and Hand Activity Inventory (8.2 point increase, scale out of 91, p < 0.01). Established criteria for clinically meaningful important differences were surpassed for both the hand function and daily living task assessments. The majority of participants provided satisfaction and usability questionnaire scores above 70%. Seven participants desired to use My-HERO in the clinic and at home during their therapy and daily routines., Conclusions: People with hand impairment after stroke value that myoelectric untethered robotic gloves enhance their motion and bimanual task performance and motivate them to use their muscles during engaging activities of daily living. They desire to use these gloves daily to enable greater independence and investigate the effects on neuromuscular recovery., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2020.)

A ballistocardiogram (BCG) is a versatile bio-signal that enables ambient remote monitoring of heart failure (HF) patients in a home setting, achieved through embedded sensors in the surrounding environment. Numerous methods of analysis are available for extracting physiological information using the BCG; however, most have been developed based on non-clinical subjects. While the difference between clinical and non-clinical populations are expected, quantification of the difference may serve as a useful tool. In this work, the differences in resting-state BCGs of the two cohorts in a sitting posture were quantified. An instrumented chair was used to collect the BCG from 29 healthy adults and 26 NYHA HF class I and II patients while seated without any stress test for five minutes. Five 20-second epochs per subject were used to calculate the waveform fluctuation metric at rest (WFMR). The WFMR was obtained in two steps. The ensemble average of the segmented BCG heartbeats within an epoch were calculated first. Mean square errors (MSE) between different ensemble average pairs were then retrieved. The MSEs were averaged to produce the WFMR. The comparison showed that the clinical cohort had higher fluctuation than the non-clinical population and had at least 82.2% separation, suggesting that greater errors may result when existing algorithms were used. The WFMR acts as a bridge that may enable important features, including the addition of error margins in parameter estimation and ways to devise a calibration strategy when resting-state BCG is unstable.

Background: Outdoor mobility is an important aspect of older adults' functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct. There is, as of yet, no classification algorithm that groups and characterizes older adults' outdoor mobility based on its semantic aspects (ie, mobility intentions and motivations) by integrating geographic and domain knowledge., Objective: This study assesses the feasibility of using GPS to determine semantic dimensions of older adults' outdoor mobility, including destinations and activity types., Methods: A total of 5 healthy individuals, aged 65 years or older, carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details of all excursions from their homes, including date, time, and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (eg, food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method., Results: Our results detected the stop locations of older adults from their GPS data with an F1 score of 87%. On average, the extracted home locations were within a 40.18-meter (SD 1.18) distance of the actual home locations. For the activity-inference algorithm, our results reached an F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference's accuracy measure differed by neighborhood characteristics (ie, Walk Score)., Conclusions: We conclude that GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this system that can be adopted in clinical practice., (©Sayeh Bayat, Gary Naglie, Mark J Rapoport, Elaine Stasiulis, Belkacem Chikhaoui, Alex Mihailidis. Originally published in JMIR Aging (http://aging.jmir.org), 28.07.2020.)

Aims and Objectives: To describe the proportion of toothbrushing task steps, long-term care residents had an opportunity to complete; the duration and quality of toothbrushing by both residents and caregivers; and the feedback caregivers provided., Background: Poor oral health is widespread among older adults in long-term care homes; however, little is known about their actual oral health practices., Design: Secondary analysis of video recordings., Methods: A total of 58 video-recorded sessions were analysed from two long-term care homes in Canada. Eligible residents had at least one natural tooth, required oral care assistance, had Alzheimer's disease and understood English. Eligible caregivers spoke English and had worked for at least 1 year with people with dementia. Toothbrushing success was identified by the resident's participation in, and completion of, nine toothbrushing steps. Total time spent brushing teeth was calculated by summing the duration of time spent brushing teeth. Quality was described by time spent brushing the facial versus the lingual or occlusal surfaces. Caregiver verbal feedback was pulled from transcripts and analysed using content analysis. STROBE guidelines were used in reporting this study., Results: The two step residents most frequently completed or attempted were brushing their teeth (77% complete, 7% attempt) and rinsing their mouth (86% complete, 2% attempt). The average time spent brushing teeth was 60.33 s (SD = 35.15). In 66% of observed videos, toothbrushing occurred only on the facial tooth surfaces, with no time spent brushing the lingual or occlusal surfaces., Conclusion: Caregivers are supporting residents to independently complete toothbrushing; however, the duration and quality of toothbrushing are not sufficient to ensure optimal oral health., Relevance to Clinical Practice: Clear, detailed guidelines are required to ensure adequate oral care for long-term care residents. Staff need to be aware that all surfaces should be brushed to ensure proper oral health., (© 2020 John Wiley & Sons Ltd.)

Background: Dementia is a major public health concern associated with significant caregiver demands and there are technologies available to assist with caregiving. However, there is a paucity of information on caregiver needs and preferences for these technologies, particularly from a sex and gender perspective. To address this gap in research, the objectives of this study are to examine (1) the knowledge of technology, (2) perceived usefulness of technology, (3) feature preferences when installing and using technology and (4) sex and gender influences on technology needs and preferences among family caregivers of persons with dementia (PWD) across North America., Methods: A secondary analysis was conducted on an existing cross-sectional survey with family caregivers of PWDs. Respondents were recruited through the Alzheimer Society of Canada, the Victorian Order of Nurses and Adult Day Programs and other Canadian health care provision institutes. Descriptive statistics, bivariate and multivariate analyses were used to describe the study sample, uncover differences between male and female caregivers and examine sex and gender influences on caregivers' technology needs and preferences., Results: A total of 381 eligible responses were received over a nine month data collection period. The majority of respondents did not know much about and never used any technologies to assist with caregiving. "Being easy to install", "easy to learn how to use" and "cost" were identified as the most important features when purchasing and setting up technology, while "reliability" was identified as the most important feature when using technology. Most respondents were willing to pay up to $500 to acquire individual technologies. Controlling for other socio-demographic variables, female respondents were more likely to have some or more knowledge about technology for caregiving while male respondents were more willing to pay higher amounts for these technologies compared to their female counterparts., Conclusions: As one of the first studies of its kind, our findings represent a step towards the incorporation of sex and gender considerations such as cost and reliability in technology design and promotion for caregivers. Future efforts are warranted to establish an in-depth understanding of sex and gender influences in relation to other social and environmental factors.

Objectives: Much is known about the demands of caregiving for persons with dementia (PWD) and its effects on family caregivers, however sex and gender aspects have received less attention. We synthesized the evidence on sex and gender distinctions in: (1) the caregiving burden and (2) the impact of caregiving on the physical and mental health of family caregivers of PWD., Design: Systematic review., Data Sources: Medline, Embase, PsycINFO and Cumulative Index to Nursing and Allied Health Literature between January 2007 and October 2019 were searched., Eligibility Criteria for Selecting Studies: Included studies met the following criteria: (1) examine experiences and/or impacts of caregiving among family caregivers of individuals with any form of dementia; (2) report sex and/or gender distribution of study population and/or report results stratified by sex and/or gender, and (3) include both male and female family caregivers., Data Extraction and Synthesis: Two independent reviewers extracted the data and assessed risk of bias using the Critical Appraisal Skills Program checklist and National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies. Data were synthesized using a narrative synthesis approach., Results: A total of 22 studies were included. Caregiving burden was measured using various methods. A majority of studies reported higher burden among females. All studies that did not report a sex and gender difference in caregiving burden accounted for confounders. Findings on sex and gender differences on physical and mental health conditions were inconsistent with most studies failing to account for confounders in their analyses., Conclusions: Current evidence on sex and gender differences in caregiving burden, mental and physical health is limited. Findings suggest presence of sex and gender differences in caregiving burden. Given the variety of mental and physical health constructs that were examined, further research is required to substantiate the evidence. PROPSERO Registration Number: CRD 42018070032., Competing Interests: The authors have declared that no competing interests exist.

Background: In long-term residential care (LTRC), caregivers' attempts to provide person-centered care can be challenging when assisting residents living with a communication disorder (eg, aphasia) and/or a language-cultural barrier. Mobile communication technology, which includes smartphones and tablets and their software apps, offers an innovative solution for preventing and overcoming communication breakdowns during activities of daily living. There is a need to better understand the availability, relevance, and stability of commercially available communication apps (cApps) that could support person-centered care in the LTRC setting., Objective: This study aimed to (1) systematically identify and evaluate commercially available cApps that could support person-centered communication (PCC) in LTRC and (2) examine the stability of cApps over 2 years., Methods: We conducted systematic searches of the Canadian App Store (iPhone Operating System platform) in 2015 and 2017 using predefined search terms. cApps that met the study's inclusion criteria underwent content review and quality assessment., Results: Although the 2015 searches identified 519 unique apps, only 27 cApps were eligible for evaluation. The 2015 review identified 2 augmentative and alternative cApps and 2 translation apps as most appropriate for LTRC. Despite a 205% increase (from 199 to 607) in the number of augmentative and alternative communication and translation apps assessed for eligibility in the 2017 review, the top recommended cApps showed suitability for LTRC and marketplace stability., Conclusions: The recommended existing cApps included some PCC features and demonstrated marketplace longevity. However, cApps that focus on the inclusion of more PCC features may be better suited for use in LTRC, which warrants future development. Furthermore, cApp content and quality would improve by including research evidence and experiential knowledge (eg, nurses and health care aides) to inform app development. cApps offer care staff a tool that could promote social participation and person-centered care., International Registered Report Identifier (irrid): RR2-10.2196/10.2196/17136., (©Rozanne Wilson, Diana Cochrane, Alex Mihailidis, Jeff Small. Originally published in JMIR Aging (http://aging.jmir.org), 08.04.2020.)

Objectives: This study evaluated the user experiences (UX) of stroke patients residing in the adaptive healing room (AHR) and compared them to the UX of patients residing in standard private rooms (SPRs)., Background: Healing environments in healthcare settings can promote patients' healing processes, outcomes, and psychological well-being. The AHR was designed as a healing environment for stroke patients and has been previously evaluated in laboratory settings. This study was the first to evaluate it in its intended context-a stroke rehabilitation unit., Methods: The UX of 10 patients residing in the AHR and 15 patients residing in SPRs were collected via structured interviews with a set of open-ended questions and analyzed using quantitative and qualitative methods., Results: The AHR design features (orientation screen, skylight, and nature view) were rated positively by most patients. The skylight emerged as the least favorable. Responses to open-ended questions revealed that UX may be further improved if patients have more control over some of the settings (e.g., light intensity and nature views), and if the system allowed for more stimulation for patients at later stages of their recovery. Additionally, the results suggest that patients in the AHR have better UX than patients in the SPRs., Conclusion: The AHR has the potential to improve UX in the stroke rehabilitation unit. Patient feedback can be used to refine the AHR before carrying out clinical trials to assess the effect of the AHR on patient outcomes (e.g., sleep, mood, and length of stay) and stroke recovery.

Background: The Hand Extension Robot Orthosis (HERO) Grip Glove was iteratively designed to meet requests from therapists and persons after a stroke who have severe hand impairment to create a device that extends all five fingers, enhances grip strength and is portable, lightweight, easy to put on, comfortable and affordable., Methods: Eleven persons who have minimal or no active finger extension (Chedoke McMaster Stage of Hand 1-4) post-stroke were recruited to evaluate how well they could perform activities of daily living and finger function assessments with and without wearing the HERO Grip Glove., Results: The 11 participants showed statistically significant improvements (p < 0.01), while wearing the HERO Grip Glove, in the water bottle grasp and manipulation task (increase of 2.3 points, SD 1.2, scored using the Chedoke Hand and Arm Inventory scale from 1 to 7) and in index finger extension (increase of 147 o , SD 44) and range of motion (increase of 145 o , SD 36). The HERO Grip Glove provided 12.7 N (SD 8.9 N) of grip force and 11.0 N (SD 4.8) of pinch force to their affected hands, which enabled those without grip strength to grasp and manipulate blocks, a fork and a water bottle, as well as write with a pen. The participants were 'more or less satisfied' with the HERO Grip Glove as an assistive device (average of 3.3 out of 5 on the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 Scale). The highest satisfaction scores were given for safety and security (4.6) and ease of use (3.8) and the lowest satisfaction scores were given for ease of donning (2.3), which required under 5 min with assistance. The most common requests were for greater grip strength and a smaller glove size for small hands., Conclusions: The HERO Grip Glove is a safe and effective tool for enabling persons with a stroke that have severe hand impairment to incorporate their affected hand into activities of daily living, which may motivate greater use of the affected upper extremity in daily life to stimulate neuromuscular recovery.

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. In this paper, we present a novel framework, DeepFall , which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras, and show superior results in comparison with traditional autoencoder methods to identify unseen falls., Competing Interests: Conflict of interestsThe authors declare that they have no conflict of interest., (© Springer Nature Switzerland AG 2019.)

Importance: Powered wheelchairs provide independence for people with mobility impairments; however, current training practices may not meet the needs of those with cognitive impairments. Shared-control teleoperation may have utility in a clinical setting when developing training suited to this population., Objective: To explore the clinical utility of a shared-control teleoperation device for powered wheelchair assessment and training., Design: In this qualitative study, we used two sequential semistructured interviews conducted a minimum of 2 wk apart. Thematic analyses were used with member checking, reflexive journaling, and triangulation of researchers to establish trustworthiness of the data., Setting: Rehabilitation center and residential care and community settings., Participants: Using purposive sampling, we recruited occupational therapists and physical therapists who were mostly female and who had a range of practice experience., Results: Fifteen participants were interviewed, and two primary themes were identified: (1) "A big enabler" described how shared control provides opportunities to train people who may otherwise be denied powered mobility, and (2) "changing the learner experience" described how shared control may promote success in skill development through an alternative learning experience., Conclusions and Relevance: Shared-control technology may have the potential to broaden the scope of therapeutic intervention by reducing risk to the driver and others in the environment and by facilitating alternative training approaches., What This Article Adds: Technological advances that allow more control over a powered wheelchair by a clinician, known as shared control, may provide learning opportunities for people who are otherwise denied access to powered mobility. Shared control may also allow the use of new instructional techniques, increase safety in the training process, and reduce anxiety associated with learning., (Copyright © 2019 by the American Occupational Therapy Association, Inc.)

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

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

Background.: Powered-wheelchair use improves participation for people with mobility limitations; however, many individuals do not receive powered-wheelchair skills training that meets their learning needs., Purpose.: The aim of this work is to evaluate the feasibility of a powered-wheelchair training program for older adults with cognitive impairment, using errorless learning strategies facilitated by shared control technology., Method.: A feasibility 2 × 2 factorial randomized controlled trial will recruit 32 older adults in residential care with mild to moderate cognitive impairment who are new powered-wheelchair use. The intervention consists of six or 12 training sessions, facilitated by shared control technology, using errorless learning techniques. Control participants will receive six or 12 training sessions using trial-and-error methods. Feasibility and clinical outcomes data (primary outcome: powered-wheelchair skills) will be collected., Implications.: Errorless learning facilitated by shared control technology may be an alternative to meet the powered-wheelchair learning needs of older adults with cognitive impairments.

Background: Concussion is a common injury among Canadian children and adolescents that leads to a range of neurobehavioral deficits. However, noticeable gaps continue to exist in the management of pediatric concussion, with poor health outcomes associated with the inadequate application of best practice guidelines., Objective: The aim of this study was to describe the development and assess the usability of a mobile phone app to aid youth in the self-management of concussion. A secondary objective was to assess the usefulness of the app., Methods: An agile user-centered design approach was used to develop the technology, followed by a formative lab-based usability study for assessment and improvement proposals. Youths aged 10 to 18 years with a history of concussion and health care professionals involved in concussion management were recruited. This study included participants performing 12 tasks with the mobile phone app while using the think aloud protocol and the administration of the System Usability Scale (SUS), posttest questionnaire, and a semistructured interview., Results: A mobile phone app prototype called NeuroCare, an easily accessible pediatric concussion management intervention that provides easy access to expert-informed concussion management strategies and helps guide youth in self-managing and tracking their concussion recovery, was developed. A total of 7 youths aged between 10 and 18 years with a history of concussion and 7 health care professionals were recruited. The mean SUS score was 81.9, mean task success rates were greater than 90% for 92% (11/12) of the tasks, 92% (11/12) of tasks had a total error frequency of less than 11 errors, and mean task completion times were less than 2 min for 100% of the tasks., Conclusions: Results suggest that participants rated this app as highly usable, acceptable to users, and that it may be useful in helping youth self-manage concussion., (©Harminder Sandhu, Katherine Wilson, Nick Reed, Alex Mihailidis. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 31.05.2019.)

The hand extension robot orthosis (HERO) glove was iteratively designed with occupational therapists and stroke survivors to enable stroke survivors with severe hand impairment to grasp and stabilize everyday objects, while being portable, lightweight, and easy to set up and use. The robot consists of a batting glove with artificial tendons embedded into the glove's fingers. The tendons are pulled and pushed by a linear actuator to extend and flex the fingers. The robot's finger extension and grasp assistance are automated using inertial measurement unit signal thresholds. Five stroke survivors (Chedoke McMaster Stroke Assessment - Stage of Hand 1-3) put on the HERO Glove in 1-3 minutes, with assistance. The stroke survivors performed significantly better on the Box and Block Test (2.8 more blocks transferred, ) while wearing the HERO Glove than when not wearing the glove. Four stroke survivors could only transfer blocks while wearing the HERO Glove. The HERO Glove enabled these stroke survivors to more fully extend their index finger (an increase of 97.5°, ) and three of five stroke survivors were better able grasp a water bottle. Therapists and stroke survivors suggested increasing the HERO Glove's grip force assistance and valued the glove's portability, lightweight design and potential usefulness in assisting with task-based therapy.