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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. [ABSTRACT FROM AUTHOR]

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%5frecord.php?RecordID=216141 International Registered Report Identifier (IRRID): RR2-10.1159/000514838 [ABSTRACT FROM AUTHOR]

To quantify the effect of multiple sclerosis (MS) on spatiotemporal gait characteristics accounting for disability severity and fall classification. 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. 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. 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. 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). 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. [ABSTRACT FROM AUTHOR]

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 weremostly 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. [ABSTRACT FROM AUTHOR]

Behavioural symptoms of dementia present a significant risk within long‐term care homes, resulting in difficulties supporting residents and monitoring their safety due to limited staffing resources. Existing video surveillance infrastructure can be used to monitor and automatically detect clinically important behaviours that can put the residents at risk. An anomaly detection approach can be used to detect behaviours of risk from videos due to their infrequent and diverse nature. However, most existing video anomaly detection approaches focus on appearance‐based features, which can put the privacy of a person at risk and is also susceptible to pixel‐based noise. In this study, we explored different privacy‐protecting approaches and present a conditional average neighbour score method to detect behaviours of risks in videos from a dementia care unit. The conditional average neighbour score method considers that behaviours of risk in people with dementia are sometimes momentary, but often they persist over time. In this study, we used semantic segmentation masks approach to protect participants' privacy by obscuring them in the video. The privacy‐protecting inputs were used to train a customized spatio‐temporal convolutional autoencoder and identify behaviours of risk as anomalies. The video was collected on a dementia care unit and composed of approximately 21 hours of training data (normal activities) and 9 hours of test data (normal activities and behaviours of risk events). In comparison to RGB input (0.822), we obtained an equivalent area under the receiver operating characteristic curve (AUC ROC) performance of 0.823 for the segmentation mask‐based privacy‐protecting approach. Further, using the conditional average neighbour score method, we improved the AUCROC performance to 0.83 for the segmentation mask‐based privacy‐protecting approach. The results signify that it is possible to use surveillance videos from a dementia care unit to detect behaviours of risk as anomalies, and that privacy protecting methods work as well as methods using the raw videos. This research paves the way to improve the quality of life of residents and reduce injuries in residential care homes, while respecting their privacy. [ABSTRACT FROM AUTHOR]

Agitation and aggression are among the most challenging symptoms of dementia. Agitated persons with dementia can harm themselves, their caregivers, or other patients in a care facility. Automatic detection of agitation would be useful to alert caregivers so that appropriate interventions can be performed. The building blocks in the automatic detection of agitation and aggression are appropriate sensing platforms and generalized predictive models. In this article, we perform a systematic review of studies that use different types of sensors to detect agitation and aggression in persons with dementia. We conclude that actigraphy shows some evidence of correlation with incidences of agitation and aggression; however, multimodal sensing has not been fully evaluated for this purpose. Based on this systematic review, we provide guidelines and recommendations for future research directions in this field. [ABSTRACT FROM AUTHOR]

Background Technological advances are leading to the ability to autonomously monitor patient's health status in their own homes, to enable aging-in-place. Objectives To understand the perceptions of seniors with heart failure (HF) regarding smart-home systems to monitor their physiological parameters. Methods In this qualitative study, HF outpatients were invited to a smart-home lab, where they completed a sequence of activities, during which the capacity of 5 autonomous sensing modalities was compared to gold standard measures. Afterwards, a semi-structured interview was undertaken. These were transcribed and analyzed using an interpretive-descriptive approach. Results Five themes emerged from the 26 interviews: (1) perceptions of technology, (2) perceived benefits of autonomous health monitoring, (3) disadvantages of autonomous monitoring, (4) lack of perceived need for continuous health monitoring, and (5) preferences for autonomous monitoring. Conclusions Patient perception towards autonomous monitoring devices was positive, lending credence to zero-effort technology as a viable and promising approach. [ABSTRACT FROM AUTHOR]

Introduction To date, potential difficulties that people with cognitive deficits meet in everyday life are relatively well known, but in which activities mastery is desired, and why, is less researched. The aim of this study was to develop deeper knowledge about activities that people with cognitive deficits want to continue mastering in everyday life and the reasons why these activities were desired. Method In the frame of a scoping study, articles were gathered and charted. Sixteen qualitative studies were selected as meeting the aim of the study and analyzed with a descriptive-interpretative method. Findings The analysis of the studies showed that the desired activities conveyed social values or independence, supported significant roles, diminished negative influence on other people, and increased health and safety. Linkages, in the form of dependencies between the desired activities, were also identified. Conclusion When planning for interventions for facilitating everyday activities, for example with technology, it is beneficial to consider both safety and values that are more closely related to meaningfulness and wellbeing. Most difficult activities were identified as hindering outdoor activities, and targeting those activities might be most valuable for enabling active everyday lives for this group. [ABSTRACT FROM AUTHOR]

Diversification of application areas, technologies, and computational techniques in the Ambient Assisted Living (AAL) domain alongside the need for a balance between reproducibility and customizability for the heterogeneous end-user groups has led towards the development of domain-specific middleware platforms, which are software frameworks that facilitate integration and communication amongst heterogeneous hardware and software components, so they can operate synergistically within a shared environment. Recent efforts that study such platforms have emphasized the need for improved evaluation methods, particularly feature-focused scenario-based evaluations. Thus, the objective of this work was to perform a comparative analysis of two prominent AAL middleware platforms and their programmability for Older Adults with Dementia (OAwD) to ultimately derive guidelines for the development of AAL systems. Mapping of a platform-independence use-case scenario to both platforms, hinted towards the need for graphical-user interfaces over text-based interfaces and application area-specific modules. [ABSTRACT FROM AUTHOR]

Background: The progression of dementia often leads to complete driving cessation, which poses major challenges for persons with dementia (PwD) and their caregivers. In response to these challenges, the use of Automated Vehicles (AV) by PwD has been considered as a way of prolonging PwD's safe driving. AVs can either be used to assist PwD with certain driving tasks, such as steering or braking (Partially Automated Vehicles; PAVs), or by performing all driving tasks (Fully Automated Vehicles; FAVs). There are unique considerations regarding the use of AVs by PwD that are currently not well‐understood. This study examined caregivers' perspective on the usefulness of AVs in addressing the driving‐related challenges faced by PwD. Method: Semi‐structured interviews were conducted with 20 primary family caregivers of PwD. Both in the interviews and using a Likert scale questionnaire, participants were asked about their attitude towards PAV and FAV use by themselves and the PwD in their care. Thematic analysis with inductive coding was used to analyse the transcribed interview data. Result: Caregivers reported significantly more negative attitudes towards PAV/FAV use by the PwD in their care compared to use by themselves (Table 1). The thematic analysis yielded two overarching types of caregiver concerns. (1) unresolved concerns about PwD's mobility that persist after PAV/FAV use: difficulty navigating tasks at the destination; AVs not providing the same sense of freedom as driving; need for caregivers' presence in the vehicle; caregivers' unawareness of PwDs' driving ability decline until a traffic incident. (2) emerging concerns specific to PAV/FAV use by PwD: PwD's confusion caused by lack of exposure to AVs; PwD's possible distress/agitation in AVs; PwD's possible inability to navigate tasks required by the AV (e.g., response to system failures, negotiating pick‐up/drop‐off locations); PwD's manual driving skill degradation upon constant use of AVs; AVs enabling PWD to wander to distant locations. Conclusion: This study helps to identify AV design targets specific to PwD. In addition, study results outline caregivers' concerns around AV use by PwD that extend beyond PwD's driving, which highlights the importance of considering a holistic perspective when addressing mobility‐related needs of PwD by introducing AVs. [ABSTRACT FROM AUTHOR]

Background: Automated vehicles (AVs) hold potential promise in sustaining the safe mobility of older adults whose driving is compromised due to cognitive impairments. However, current AVs have an operational limit and when this limit is reached, the driver is expected to promptly take over driving control. The timely performance of takeover task draws upon cognitive resources that can be compromised in people with dementia (PwD) and people with Mild Cognitive Impairments (PwMCI). Therefore, this study investigates the abilities of PwD and PwMCI to perform an AV driving takeover task. Method: Population. 20 participants per three groups of PwD, PwMCI, and older adult controls will be recruited. Currently, the data collection for the controls and recruitment for the PwD and PwMCI are on‐going. The present results are based on seven control participants who have completed the study to date. Study Design & Setting. Participants completed a battery of sensory and cognitive tests and performed takeover tasks in response to an audio‐visual alert in a simulated AV using a high‐fidelity driving simulator. Each participant performed takeovers in eight driving conditions varying in lighting level (day/night), road structure (straight/curved), and speed limit (60/120 km/h). Measures. To characterize participants' takeover abilities, their response time was measured as the time interval between the audio‐visual alert and uptakes in speed (longitudinal takeover time), and changes in steering wheel angle (lateral takeover time). Result: A correlation analysis was conducted between each of takeover time measures and cognitive and visual tests. MoCA scores showed a significant, negative correlation with lateral takeover time (rk = ‐0.85, p =.015, 95% CI [‐0.98, ‐0.27]). To investigate the effects of driving condition on takeover time, pairwise Kruskal‐Wallis tests were conducted on measures of takeover time as separated by driving condition. Significant differences were only observed in lateral takeover time in the curved, high‐speed condition compared to other conditions (χ2(3) = 29.46, p <.001). Conclusion: This study will help clarify the associations between older adults' cognitive abilities and takeover abilities in AVs which can, in turn, inform the guidelines around the safe use of AVs by PwD and PwMCI. [ABSTRACT FROM AUTHOR]

Background: As the world grapples with the COVID‐19 pandemic, there have been widespread disruptions to everyday life due to social distancing. Older adults with Alzheimer disease (AD) are at increased risk of morbidity and mortality from COVID‐19. It is unknown how COVID‐19 affects the mobility patterns of older adults with preclinical AD. Since before the pandemic, we have been monitoring the driving behaviors of older adults, enabling us to evaluate the impact of the pandemic on individuals with and without preclinical AD. Method: We used in‐vehicle Global Positioning System (GPS) devices to study driving behaviors of 115 older adults enrolled in the DRIVES study (aged 65+) from 1/1/2019 to 31/12/2020. The cohort included 62 individuals with preclinical AD (PreAD) and 53 without preclinical AD (CTL), as determined by cerebrospinal fluid biomarkers. All participants completed an online survey about their overall experiences during the pandemic. Using the GPS data, we determined the average monthly distance travelled, and the number of visitations to destinations categorized as food shopping, place of worship, restaurant, leisure, or health. All measures were computed monthly. Result: oth groups experienced an approximate 40% decline in average monthly distance travelled overall after the start of the pandemic (PreAD: 1287.92 to 783.38 km vs. CTL: 1751.26 to 1053.29 km). Visits to places of worship, restaurants, leisure and health places declined by 70%, 46%, 23%, and 23% for the PreAD group, and by 48%, 31%, 48%, and 22% for the CTL group, respectively. However, the pandemic did not result in a significant decline in Food Shopping among either of the groups. Overall, compared to the CTL group, the PreAD group experienced a higher level of stress in response to the recommendations for socially distancing (p<0.01), more uncertainty about their risk of COVID‐19 (p<0.05), more decline in trips for worship (p<0.05) and less decline in trips for leisure (p<0.01). Conclusion: Our findings indicate decreased mobility in all older adults during the pandemic, with the preclinical AD group exhibiting more decline in trips to places of worship, less decline in leisure activities, and increased stress and uncertainty in response to COVID‐19. [ABSTRACT FROM AUTHOR]

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. J Med Internet Res 2022;24(6):e34307 doi:10.2196/34307 [ABSTRACT FROM AUTHOR]

Maintaining an active lifestyle and participating in social activities are key components of healthy ageing. These components rely on the individuals' ability to remain mobile out of home. The global positioning system (GPS) is increasingly used to assess outdoor mobility of older adults. However, there is a gap in establishing a framework that recognizes the differences between mobility patterns of cognitively intact older adults and older adults with dementia (OAwD). We propose a set of GPS-derived metrics to identify these differences, which can be used to evaluate changes in mobility patterns of OAwD over time. We analyzed the mobility profiles of 15 older adults from the greater Toronto area using GPS technology. Participants were aged between 65 and 90 years (M=74.9, SD=7.05) and were either cognitively healthy (n = 8) or diagnosed with dementia (n = 7). First, we created GPS-based features representing different dimensions of outdoor mobility. These features include (1) the typical distance covered by individuals, (2) randomness of the travel patterns, (3) the spatial variation of the GPS locations, (4) the number of distinct stops visited per day, (5) the number of daily trips away from home, (6) the score of three levels of outdoor life-space, and (7) the maximum distance travelled from home. Then, we used Recursive Feature Elimination (RFE) with Random Forest (RF) to select the most important mobility features with respect to cognitive status. A total of 55,580 GPS points were collected by the 15 unique participants over a period of 4 to 8 weeks. The RFE method indicated that features (1), (2), (3), and (5) had the strongest association with cognitive status. Our results suggested that OAwD displayed more predictability (i.e. smaller randomness) in travel patterns compared to the controls (OAwD: M=4.82, SD=0.57 vs. CTL:M=5.62, SD=0.71). Furthermore, the OAwD made fewer daily out-of-home trips compared to the controls (OAwD: M=1.06, SD=0.79 vs. CTL: M=1.65, SD=0.45), and covered smaller distances compared to the controls (OAwD: M=12.27, SD=11.73 km vs. CTL: M=15.78, SD=24.31 km). The proposed set of GPS-based features identified the differences between mobility patterns of cognitively intact older adults and OAwD. Canadian Consortium on Neurodegeneration in Aging [ABSTRACT FROM AUTHOR]

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Abstract: This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregiver’s help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-tracking system by comparing its outputs to manual annotations and to a simple hand-detection method. Second, we test the POMDP solution methods in simulation, and show that our policies have higher expected return than five other heuristic methods. Third, we report results from a ten-week trial with seven persons moderate-to-severe dementia in a long-term care facility in Toronto, Canada. The subjects washed their hands once a day, with assistance given by our automated system, or by a human caregiver, in alternating two-week periods. We give two detailed case study analyses of the system working during trials, and then show agreement between the system and independent human raters of the same trials. [Copyright &y& Elsevier]

Background: People with dementia (PwD) often become lost, which is commonly attributed to spatial disorientation, one of the earliest symptoms of dementia, particularly of the Alzheimer's disease type. Spatial disorientation can limit a person's ability to navigate in an outdoor environment. As a result, PwD experience outdoor mobility decline, which, in turn, can have a negative impact on their cognitive functions. Thus, enabling safe outdoor mobility is important for dementia research. Artificial Intelligence (AI) methods in conjunction with Global Positioning System (GPS) data show great potential for supporting the outdoor mobility needs of PwD. The objective of this work is to evaluate the extent to which we can predict future destinations of PwD by learning from their past mobility habits. Method: Eight cognitively‐intact older adults (CTL) and seven older adults with dementia completed four weeks of GPS recording. Each participant's stops and trips were extracted from their trajectories. We determined the predictability of each participant's mobility patterns using three approaches. First, we assumed each stop is visited with equal probability, thus, disregarding temporal aspects of travel. Next, we built on the previous approach by including stop visitation probability, thus, capturing the heterogeneity of visitations. Finally, to capture the full spatiotemporal characteristics of mobility, we added in‐depth temporal characteristics including the visitation frequency, the order in which the stops were visited, and the time spent at each stop. Result: Relying solely on the spatial dimension of mobility yielded no predictive power across the two groups. Adding the heterogeneity of visitation patterns, we observed an increase in the predictability power; PwD displayed a trend toward higher predictability compared to the CTLs, but the student's t‐test did not reach statistical significance (0.833±0.085vs.0.768±0.029, t(13)=‐2.06, p=0.06; d=‐1.07). Finally, relying on full spatiotemporal characteristics, a 4‐week record of mobility patterns displayed 95% (SD=2%) and 92% (SD=1%) predictability among PwD and CTLs, respectively. This value was significantly higher among PwD, t(13) = ‐3.39, p<.01; d=‐1.75. Conclusion: Our findings offer new perspectives on the predictive mobility models based on GPS data and AI that can be used to provide personalized assistance for outdoor navigation of people with dementia. [ABSTRACT FROM AUTHOR]

Background: Alzheimer disease (AD) is the most prevalent form of age‐related dementia. The clinical manifestation of AD is generally preceded by a silent preclinical phase during which early AD brain changes are present but dementia symptoms have not yet appeared. Molecular biomarkers have been used to ascertain the presence of AD brain changes, which are obtained via imaging and lumbar puncture. However, the widespread use of these methods is limited by cost and availability. Therefore, there is a need for a non‐invasive and low‐cost solution for identifying individuals who are likely to have preclinical AD. Since the preclinical phase of AD has been shown to impact driving, daily driving behaviours captured using Global Positioning System (GPS) devices can serve as a digital biomarker to detect preclinical AD. The objective of the present study is to use machine learning methods to evaluate the ability of in‐vehicle GPS devices to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD. Method: We used commercial in‐vehicle GPS devices to study the naturalistic driving behaviours of 144 cognitively normal older drivers (aged 65+) over one year. The cohort included 69 individuals with and 75 without preclinical AD, as determined by cerebrospinal fluid (CSF) biomarkers. Four Random Forest (RF) models were trained with three sets of variables: (1) driving features only, (2) driving features and age, and (3) driving features, age and APOE ε4 status. Finally, the strongest predictors of preclinical AD were identified using an RF‐based Recursive Feature Elimination technique. Result: The F1 score of the RF models for identifying preclinical AD was 82% using GPS‐based driving indicators, 88% using age and driving indicators, and 91% using age, APOE ε4 status and driving. The area under the receiver operating curve for the final model was 0.96. APOE ε4 status and age were the two most important features for predicting preclinical AD, and the most important driving feature was the vehicle's jerk, which is a measure of driving smoothness. Conclusion: Driving behaviours captured with GPS can accurately distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD. [ABSTRACT FROM AUTHOR]