KITE Research Institute

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: 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.

Purpose Judgments regarding hyoid movement are frequently included in evaluations of swallowing. However, the literature lacks reference values for measures of hyoid kinematics in healthy swallowing. This study explores hyoid movement across the continuum from thin to extremely thick liquids. Method Participants were 39 healthy adults under the age of 60 years (19 men) who underwent videofluoroscopy involving three sips each of 20% w/v thin barium and six sips each of slightly, mildly, moderately, and extremely thick barium. Half of the thickened stimuli were prepared using xanthan gum; and half, with a starch-based thickener. Sip volume was derived from pre- and post-sip cup weights. Hyoid position was tracked frame-by-frame relative to the anterior–inferior corner of C4. Measures of peak hyoid position (along the XY axis) were normalized to a C2–C4 scalar, and measures of time-to-peak position, speed, and time-to-peak speed were derived. As a first step, Spearman's correlations confirmed the influence of sip volume on these hyoid measures. Linear mixed-effects models then explored the effects of stimulus, sip volume, and task repetition on the dependent variables. Results The data set comprised 975 swallows with available hyoid tracking data. Sip volume was correlated with peak hyoid XY position ( r s = .15, p < .01), time-to-peak position ( r s = −.15, p < .05), and speed ( r s = .13, p < .01). No significant differences in hyoid kinematics were found across stimuli. Conclusion Measures of hyoid movement in healthy swallowing remain stable across the range from thin to extremely thick liquids with no systematic alterations in hyoid position or kinematics.

BACKGROUND: The prevention of lower extremity fractures and fracture-related morbidity and mortality is a critical component of health services for adults living with chronic spinal cord injury (SCI). METHODS: Established best practices and guideline recommendations are articulated in recent international consensus documents from the International Society of Clinical Densitometry, the Paralyzed Veterans of America Consortium for Spinal Cord Medicine and the Orthopedic Trauma Association. RESULTS: This review is a synthesis of the aforementioned consensus documents, which highlight the pathophysiology of lower extremity bone mineral density (BMD) decline after acute SCI. The role and actions treating clinicians should take to screen, diagnose and initiate the appropriate treatment of established low bone mass/osteoporosis of the hip, distal femur or proximal tibia regions associated with moderate or high fracture risk or diagnose and manage a lower extremity fracture among adults with chronic SCI are articulated. Guidance regarding the prescription of dietary calcium, vitamin D supplements, rehabilitation interventions (passive standing, functional electrical stimulation (FES) or neuromuscular electrical stimulation (NMES)) to modify bone mass and/or anti-resorptive drug therapy (Alendronate, Denosumab, or Zoledronic Acid) is provided. In the event of lower extremity fracture, the need for timely orthopedic consultation for fracture diagnosis and interprofessional care following definitive fracture management to prevent health complications (venous thromboembolism, pressure injury, and autonomic dysreflexia) and rehabilitation interventions to return the individual to his/her pre-fracture functional abilities is emphasized. CONCLUSIONS: Interprofessional care teams should use recent consensus publications to drive sustained practice change to mitigate fracture incidence and fracture-related morbidity and mortality among adults with chronic SCI.

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.

Augmented Reality (AR) headsets could significantly improve the passenger experience, freeing users from the restrictions of physical smartphones, tablets and seatback displays. However, the confined space of public transport and the varying proximity to other passengers may restrict what interaction techniques are deemed socially acceptable for AR users - particularly considering current reliance on mid-air interactions in consumer headsets. We contribute and utilize a novel approach to social acceptability video surveys, employing mixed reality composited videos to present a real user performing interactions across different virtual transport environments. This approach allows for controlled evaluation of perceived social acceptability whilst freeing researchers to present interactions in any simulated context. Our resulting survey (N=131) explores the social comfort of body, device, and environment-based interactions across seven transit seating arrangements. We reflect on the advantages of discreet inputs over mid-air and the unique challenges of face-to-face seating for passenger AR.

Abstract 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.

The increased democratization of the creation, implementation, and attendance of academic conferences has been a serendipitous benefit of the movement toward virtual meetings. The Coronavirus Disease 2019 (COVID-19) pandemic has accelerated the transition to online conferences and, in parallel, their democratization, by necessity. This manifests not just in the mitigation of barriers to attending traditional physical conferences but also in the presentation of new, and more importantly attainable, opportunities for young scientists to carve out a niche in the landscape of academic meetings. Here, we describe an early "proof of principle" of this democratizing power via our experience organizing the Canadian Computational Neuroscience Spotlight (CCNS; crowdcast.io/e/CCNS), a free 2-day virtual meeting that was built entirely amid the pandemic using only virtual tools. While our experience was unique considering the obstacles faced in creating a conference during a pandemic, this was not the only factor differentiating both our experience and the resulting meeting from other contemporary online conferences. Specifically, CCNS was crafted entirely by early career researchers (ECRs) without any sponsors or partners, advertised primarily using social media and "word of mouth," and designed specifically to highlight and engage trainees. From this experience, we have distilled "10 simple rules" as a blueprint for the design of new virtual academic meetings, especially in the absence of institutional support or partnerships, in this unprecedented environment. By highlighting the lessons learned in implementing our meeting under these arduous circumstances, we hope to encourage other young scientists to embrace this challenge, which would serve as a critical next step in further democratizing academic meetings.

ObjectiveTo systematically assess the reporting of sex, and the percentage of female participants in randomized controlled trials (RCTs) examining interventions for the post-stroke rehabilitation of upper extremity (UE) motor disorders.Data SourcesCINAHL, Embase, PubMed, Scopus and Web of Science were searched from 1960 to April 1, 2021. Additional articles were identified using the Evidence-Based Review of Stroke Rehabilitation (EBRSR).Study SelectionStudies were eligible for inclusion if they (1) were RCTs or crossovers published in English (2) ≥50% of participants were diagnosed and affected by stroke, 3) included adults ≥18 years old, and (4) applied an intervention to the hemiparetic UE as the primary objective of the study.Data ExtractionTwo investigators independently screened the title and abstracts, and the duplicates were removed. A full text review was done for studies which met all inclusion criteria. Data was extracted using a custom data extraction template in Covidence and transferred to online Excel (V16) for data management. Study characteristics and extracted variables were summarized using standard descriptive statistics. Data analyses were performed using SPSS (V29.0).Data synthesis1,276 RCTs met inclusion criteria, and of these, 5.2% did not report results on sex accounting for 5.6% of participants. Females have been underrepresented in stroke RCTs, accounting for 38.8% of participants. Female participation was greater in acute post-stroke phase than chronic and subacute phases. Over almost five decades, there has been a small decrease in the proportion of female participants in these trials.ConclusionsEvidence-based medicine for the treatment and prevention of stroke is guided by results from RCTs. Generalizability depends on sufficient representation in clinical trials. Stakeholders, such as funders and journal editors, play a key role in encouraging researchers to enroll enough of both sexes and to report the presence or absence of sex differences in RCTs. To systematically assess the reporting of sex, and the percentage of female participants in randomized controlled trials (RCTs) examining interventions for the post-stroke rehabilitation of upper extremity (UE) motor disorders. CINAHL, Embase, PubMed, Scopus and Web of Science were searched from 1960 to April 1, 2021. Additional articles were identified using the Evidence-Based Review of Stroke Rehabilitation (EBRSR). Studies were eligible for inclusion if they (1) were RCTs or crossovers published in English (2) ≥50% of participants were diagnosed and affected by stroke, 3) included adults ≥18 years old, and (4) applied an intervention to the hemiparetic UE as the primary objective of the study. Two investigators independently screened the title and abstracts, and the duplicates were removed. A full text review was done for studies which met all inclusion criteria. Data was extracted using a custom data extraction template in Covidence and transferred to online Excel (V16) for data management. Study characteristics and extracted variables were summarized using standard descriptive statistics. Data analyses were performed using SPSS (V29.0). 1,276 RCTs met inclusion criteria, and of these, 5.2% did not report results on sex accounting for 5.6% of participants. Females have been underrepresented in stroke RCTs, accounting for 38.8% of participants. Female participation was greater in acute post-stroke phase than chronic and subacute phases. Over almost five decades, there has been a small decrease in the proportion of female participants in these trials. Evidence-based medicine for the treatment and prevention of stroke is guided by results from RCTs. Generalizability depends on sufficient representation in clinical trials. Stakeholders, such as funders and journal editors, play a key role in encouraging researchers to enroll enough of both sexes and to report the presence or absence of sex differences in RCTs.

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.

PURPOSE: Research remains equivocal regarding the links between hyoid movement and penetration-aspiration. The aims of this study were (a) to explore associations between hyoid parameters, laryngeal vestibule closure (LVC) parameters, and penetration-aspiration on thin liquids; and (b) to determine which of these parameters are the strongest predictors of penetration-aspiration. METHOD: This study involved retrospective analysis of an existing videofluoroscopy data set, collected in 305 participants (152 males) with noncongenital/nonsurgical/non-oncological risk for dysphagia. We extracted data for six thin liquid swallows per participant, and obtained measures of hyoid movement (peak position, speed) and LVC (complete/incomplete, timing, duration). Resulting values were coded as typical/atypical relative to healthy reference data. Relationships were explored using chi-square tests and odds ratios (a) for the entire data set and (b) for the subset of data with complete LVC. Hierarchical logistic regression models determined the strongest predictors of penetration-aspiration. RESULTS: Significant associations were found between penetration-aspiration and incomplete LVC, prolonged time-to-most-complete-LVC, short LVC duration, reduced anterior hyoid peak position, and reduced hyoid speed. Hyoid measures were also significantly associated with LVC parameters. In the first regression model, incomplete LVC and prolonged time-to-most-complete-LVC were the only significant predictors of penetration-aspiration. For cases with complete LVC, the only significant predictor was prolonged time-to-most-complete-LVC. CONCLUSIONS: Although reduced anterior hyoid peak position and speed are associated with penetration-aspiration on thin liquids, these measures do not independently account for penetration-aspiration when considered in conjunction with measures of LVC. When identifying mechanisms explaining penetration-aspiration, clinicians should focus on LVC (complete/incomplete) and timeliness of LVC.

PURPOSE: Prior studies suggest there may be differences in videofluoroscopic measures of swallowing across different barium concentrations. Whether different barium products of identical concentration result in similar swallowing physiology remains unknown. This is important, as barium intended for videofluoroscopy (i.e., Bracco Varibar) is not available globally. Our aim was to identify differences in healthy swallowing across five different barium stimuli. METHOD: Twenty healthy adults (10 women), aged 22-54 years, underwent videofluoroscopy including comfortable sips of thin liquid barium: two sips of 20% weight-to-volume (w/v) barium prepared with E-Z-HD powder, and two sips each of 20%w/v and 40%w/v barium prepared with Liquid Polibar Plus and E-Z-Paque powder. Recordings were analyzed according to the Analysis of Swallowing Physiology: Events, Kinematics and Timing Method. Measures of timing, kinematics and residue were obtained. Chi-square, Friedman's, and Wilcoxon signed-ranks test were used to identify differences across stimuli. RESULTS: Significant differences were seen across barium stimuli for upper esophageal sphincter (UES) opening duration, UES diameter, pharyngeal area at maximum constriction, and residue. In all cases, smaller values were seen with the 20%w/v E-Z-HD stimulus; however, this stimulus had questionable opacity for visualization. Patterns of residue severity were not explained by barium concentration. CONCLUSIONS: This study confirms that some measures of swallowing are influenced by barium product and/or concentration. Measures are not necessarily similar across different barium products at the same concentration. This study illustrates the importance of using standard and appropriate stimuli in videofluoroscopy, and for clinicians to report not only the product but also the concentration of stimuli used. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.20669712.

BACKGROUND: The COVID-19 pandemic initially led to discontinuation of the "traditional" center-based cardiac rehabilitation (CR) model. Virtual models emerged as an opportunity to deliver care, with many programs continuing to offer these models. OBJECTIVE: The aim of this study was to explore patients' perceptions of virtual models of either hybrid (combining center-based and virtual) or virtual-only CR since the pandemic. METHODS: Men and women who chose to participate in hybrid or virtual CR models between January 2022 and January 2023 were invited to attend 1 of 8 focus group sessions. Focus groups were conducted online until thematic saturation was reached. Transcripts were analyzed using thematic analysis. RESULTS: Twenty-three patients (48% female; 83% attending hybrid CR) participated in the study. Analysis revealed 12 overarching themes associated with the CR patient journey: pre-CR, namely, (1) importance of endorsement from healthcare providers and (2) need for education/communication while waiting for program initiation; during CR, namely, (3) preference for class composition/structure, (4) need to enhance peer support in the virtual environment, (5) convenience and concerns with virtual sessions, (6) necessity of on-site sessions, (7) safety of the exercise prescription, (8) requirement/obligation for allied health offerings, (9) satisfaction with virtual education, and (10) use of technology to facilitate CR participation; and post-CR, namely, (11) acknowledgment of program completion and (12) need for support/education after program graduation. CONCLUSIONS: Patients require ongoing support from time of referral to beyond CR program completion. Physical, psychosocial, nutritional, and educational supports are needed. Perceptions expressed by patients related to the program model are modifiable, and strategies to address these perceptions should be explored.

Environment sensing and recognition can improve the safety and autonomy of human-robot locomotion, especially during transitions between environmental states such as walking to and from stairs. However, accurate and real-time perception on edge devices with limited computational resources is an open problem. Here we present the development and mobile deployment of StairNet, a vision-based stair recognition system powered by deep learning. Building on ExoNet, the largest open-source dataset of egocentric images of real-world walking environments, we designed a new dataset for stair recognition with over 515,000 images. We trained a lightweight and efficient convolutional neural network for image classification, which predicted complex stair environments with 98.4% accuracy. We also studied different model compression optimization methods and deployed our system on several mobile devices running a custom-designed iOS application with onboard accelerators using CPU, GPU, and/or NPU backend computing. Of the designs that we studied, our highest performing system showed negligible reductions in classification accuracy due to model conversion for mobile deployment and achieved an inference time of 2.75 ms. The high speed and accuracy of StairNet on edge devices opens new opportunities for environment-adaptive control of robotic prosthetic legs, exoskeletons, and other assistive technologies for human locomotion.

STUDY DESIGN: Cross-sectional survey. OBJECTIVES: Degenerative cervical myelopathy (DCM) is a common syndrome of acquired spinal cord impairment caused by canal stenosis secondary to arthritic changes of the spine. International guidelines consider physiotherapy an option for mild, stable DCM; however, few studies have been conducted on nonoperative management. The objective was to determine current usage and perceptions of nonoperative physiotherapy for DCM. METHODS: Persons with DCM were recruited to a web-based survey. Participants with complete responses that had not received surgery were included (n = 167). Variables included symptom duration, treatment history, current disability, and demographic characteristics. RESULTS: Disease and demographic characteristics were equivalent between those who did and did not receive physiotherapy. In all, 19.5% of physiotherapy recipients reported subjective benefit from physiotherapy. Those perceiving benefit had significantly higher mJOA (modified Japanese Orthopaedic Association) scores, lower neck pain scores, and shorter symptom duration. In multivariate logistic regression analysis, those with mild DCM were more likely to perceive benefit than those with severe DCM, as were those with moderate DCM (to a lesser extent). Individuals whose diagnosis was delayed 1 to 2 years were less likely to perceive benefit than those that waited 0 to 6 months. CONCLUSIONS: The provision of nonoperative physiotherapy in the management of DCM is inconsistent and appears to differ from international guidelines. Few patients perceived benefit from physiotherapy; however, this was more likely in those with mild DCM and in those with shorter symptom durations. Further work is needed to establish the appropriate role of physiotherapy for this population.

Abstract Environment sensing and recognition can allow humans and robots to dynamically adapt to different walking terrains. However, fast and accurate visual perception is challenging, especially on embedded devices with limited computational resources. The purpose of this study was to develop a novel pair of AI-powered smart glasses for onboard sensing and recognition of human-robot walking environments with high accuracy and low latency. We used a Raspberry Pi Pico microcontroller and an ArduCam HM0360 low-power camera, both of which interface with the eyeglass frames using 3D-printed mounts that we custom-designed. We trained and optimized a lightweight and efficient convolutional neural network using a MobileNetV1 backbone to classify the walking terrain as either indoor surfaces, outdoor surfaces (grass and dirt), or outdoor surfaces (paved) using over 62,500 egocentric images that we adapted and manually labelled from the Meta Ego4D dataset. We then compiled and deployed our deep learning model using TensorFlow Lite Micro and post-training quantization to create a minimized byte array model of size 0.31MB. Our system was able to accurately predict complex walking environments with 93.6% classification accuracy and had an embedded inference speed of 1.5 seconds during online experiments using the integrated camera and microcontroller. Our AI-powered smart glasses open new opportunities for visual perception of human-robot walking environments where embedded inference and a low form factor is required. Future research will focus on improving the onboard inference speed and miniaturization of the mechatronic components.

Abstract Robotic prosthetic legs and exoskeletons require real-time and accurate estimation of the walking environment for smooth transitions between different locomotion mode controllers. However, previous studies have mainly been limited to static image classification, therein ignoring the temporal dynamics of human-robot locomotion. Motivated by these limitations, here we developed several state-of-the-art temporal convolutional neural networks (CNNs) to compare the performances between static vs. sequential image classification of real-world walking environments (i.e., level-ground terrain, incline stairs, and transitions to and from stairs). Using our large-scale image dataset, we trained a number of encoder networks such as VGG, MobileNetV2, ViT, and MobileViT, each coupled with a temporal long short-term memory (LSTM) backbone. We also trained MoViNet, a new video classification model designed for mobile and embedded devices, to further compare the performances between 2D and 3D temporal deep learning models. Our 3D network outperformed all the hybrid 2D encoders with LSTM backbones and the 2D CNN baseline model in terms of classification accuracy, suggesting that network architecture can play an important role in performance. However, although our 3D neural network achieved the highest classification accuracy, it had disproportionally higher computational and memory storage requirements, which can be disadvantageous for real-time control of robotic leg prostheses and exoskeletons with limited onboard resources.

BACKGROUND: Older adults with cognitive impairment are frequently hospitalized and discharged to facility-based transitional care programs (TCPs). However, it is unknown whether TCPs are effective in improving their functional status and promoting discharge home rather than to long-term care. The aims of this systematic review were to examine the effectiveness of facility-based TCPs on functional status, patient and health services outcomes for older adults (≥ 65 years) with cognitive impairment and to determine what proportion post TCP are discharged home compared to long-term care. METHODS: The Joanna Briggs Institute Critical Appraisal Manual for Evidence Synthesis was used to guide the methodology for this review. The protocol was published in PROSPERO (registration number CRD42021257870). MEDLINE, CINAHL, PsycINFO, the Cochrane Library, and EMBASE databases, and ClinicalTrials.gov and the World Health Organization Trials Registry were searched for English publications. Studies that met the following criteria were included: community-dwelling older adults ≥ 65 years who participated in facility-based TCPs and included functional status and/or discharge destination outcomes. Studies with participants from nursing homes and involved rehabilitation programs or transitional care in the home or in acute care, were excluded. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklists. Results are in narrative form. RESULTS: Twenty-two studies (18 cohort and four cross sectional studies) involving 4,013,935 participants met inclusion criteria. The quality of the studies was mostly moderate to good. Improvement in activities of daily living (ADLs) was reported in eight of 13 studies. Between 24.4%-68% of participants were discharged home, 20-43.9% were hospitalized, and 4.1-40% transitioned to long-term care. Review limitations included the inability to perform meta-analysis due to heterogeneity of outcome measurement tools, measurement times, and patient populations. CONCLUSIONS: Facility-based TCPs are associated with improvements in ADLs and generally result in a greater percentage of participants with cognitive impairment going home rather than to long-term care. However, gains in function were not as great as for those without cognitive impairment. Future research should employ consistent outcome measurement tools to facilitate meta-analyses. The level of evidence is level III-2 according to the National Health and Medical Research Council for cohort and cross-sectional studies.

The present study provides a validated ICU checklist for clinicians and researchers working with Laurentian French speakers when assessing discourse with the picnic scene of the WAB-R. It also addresses the gap in available psychometric data regarding inter-rater and test-retest reliability in PWBI.

OBJECTIVE: The benefits of aerobic exercise early after stroke are well known, but concerns about cardiovascular risk are a barrier to clinical implementation. Symptom-limited exercise testing with electrocardiography (ECG) is recommended but not always feasible. The purpose of this study was to determine the frequency of and corresponding exercise intensities at which ECG abnormalities occurred during submaximal exercise testing that would limit safe exercise prescription beyond those intensities. METHODS: This study was a retrospective analysis of ECGs from 195 patients who completed submaximal exercise testing during stroke rehabilitation. A graded submaximal exercise test was conducted with a 5- or 12-lead ECG and was terminated on the basis of predetermined endpoint criteria (heart rate, perceived exertion, signs, or symptoms). ECGs were retrospectively reviewed for exercise-induced abnormalities and their associated heart rates. RESULTS: The peak heart rate achieved was 65.4% (SD = 10.5%) of the predicted maximum heart rate or 29.1% (SD = 15.5%) of the heart rate reserve (adjusted for beta-blocker medications). The test was terminated more often because of perceived exertion (93/195) than because of heart rate limits (60/195). Four patients (2.1%) exhibited exercise-induced horizontal or downsloping ST segment depression of ≥1 mm. Except for 1 patient, the heart rate at test termination was comparable with the heart rate associated with the onset of the ECG abnormality. CONCLUSION: A graded submaximal exercise test without ECG but with symptom monitoring and conservative heart rate and perceived exertion endpoints may facilitate safe exercise intensities early after stroke. Symptom-limited exercise testing with ECG is still recommended when progressing to higher intensity exercise. IMPACT: Concerns about cardiovascular risk are a barrier to physical therapists implementing aerobic exercise in stroke rehabilitation. This study showed that, in the absence of access to exercise testing with ECG, submaximal testing with conservative heart rate and perceived exertion endpoints and symptom monitoring can support physical therapists in the safe prescription of aerobic exercise early after stroke. LAY SUMMARY: It is recommended that people with stroke participate in aerobic exercise as early as possible during their rehabilitation. A submaximal exercise test with monitoring of heart rate, perceived exertion, blood pressure, and symptoms can support physical therapists in safely prescribing that exercise.