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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.