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Abstract Background Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of test duration, the number of sensors, and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers. Methods Twelve volunteers without any prior history of balance disorders were enrolled in the study. They were asked to perform two 2-min quiet standing tests with two different testing conditions (eyes open and eyes closed). Five inertial measurement units (IMUs) were employed to capture postural sway data from each participant. IMUs were attached to the participants’ right legs, the second sacral vertebra, sternum, and the left mastoid processes. Balance parameters of interest were calculated for the single head, sternum, and sacrum accelerometers, as well as, a three-sensor combination (leg, sacrum, and sternum). Accelerometer data were used to estimate COP-based and COM-based balance parameters during quiet standing. To examine the effect of test duration and sensor location, each 120-s recording from different sensor locations was segmented into 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-, 100-, and 110-s intervals. For each of these time intervals, time- and frequency-domain balance parameters were calculated for all sensor locations. Results Most COM-based and COP-based balance parameters could be derived reliably for clinical applications (Intraclass-Correlation Coefficient, ICC ≥ 0.90) with a minimum test duration of 70 and 110 s, respectively. The exceptions were COP-based parameters obtained using a sacrum-mounted sensor, especially in the eyes-closed condition, which could not be reliably used for clinical applications even with a 120-s test duration. Conclusions Most standing balance parameters can be reliably measured using a single head- or sternum-mounted sensor within a 120-s test duration. For other sensor locations, the minimum test duration may be longer and may depend on the specific test conditions.

Objective Fatigue, headache, problems sleeping and numerous other symptoms have been reported to be associated with long COVID. However, many of these symptoms coincide with symptoms reported by the general population, possibly exacerbated by restrictions/precautions experienced during the COVID-19 pandemic. This study examines the symptoms reported by individuals who tested positive for COVID-19 compared with those who tested negative.Design Observational study.Setting The study was conducted on adult residents in Alberta, Canada, from October 2021 to February 2023.Participants We evaluated self-reported symptoms in 7623 adults with positive COVID-19 tests and 1520 adults who tested negative, using surveys adapted from the internationally standardised International Severe Acute Respiratory and emerging Infection Consortium (ISARIC)-developed COVID-19 long-term follow-up tools. These individuals had an index COVID-19 test date between 1 March 2020 and 31 December 2022 and were over 28 days post-COVID-19 testing.Primary outcome measures The primary outcomes were to identify the symptoms associated with COVID-19 positivity and risk factors for reporting symptoms.Results Fatigue was the top reported symptom (42%) among COVID-19-positive respondents, while headache was the top reported symptom (32%) in respondents who tested negative. Compared with those who tested negative, COVID-19-positive individuals reported 1.5 times more symptoms and had higher odds of experiencing 31 out of the 40 listed symptoms during the postinfectious period. These symptoms included olfactory dysfunction, menstruation changes, cardiopulmonary and neurological symptoms. Female sex, middle age (41–55 years), Indigeneity, unemployment, hospital/intensive care unit (ICU) admission at the time of testing and pre-existing health conditions independently predicted a greater number and variety of symptoms.Conclusions Our results provide evidence that COVID-19 survivors continue to experience a significant number and variety of symptoms. These findings can help inform targeted strategies for the unequally affected population. It is important to offer appropriate management for symptom relief to those who have survived the acute COVID-19 illness.

Abstract Background Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. Objective To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. Methods Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. Results A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. Conclusions Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

BackgroundCOVID-19 disrupted services received by persons with spinal cord injury (SCI) worldwide. The International Disability Alliance declared the need for a disability-inclusive response to the COVID-19 crisis, as decreased access to health care services for individuals living with varying levels of function was unacceptable. As a result, an SCI community in Canada created a novel webinar-based strategy aimed at improving access to self-management information for people living with SCI and other stakeholders. However, although telehealth practices have previously been used effectively in SCI management and rehabilitation, little to no scholarship has investigated the outcomes of implementing a webinar-based telehealth strategy in this population. ObjectiveThis study aims to understand the outcomes of implementing the webinar series. Specifically, the authors aimed to determine the reach of the series; understand its impact on social connectedness, perceptions of disability, and overall quality of interactions among persons with SCI, their families, service providers, and the public at large; and explore the long-term sustainability of the initiative. MethodsThe authors implemented a community-based participatory strategy to define a convergent mixed methods design to triangulate qualitative and quantitative data collected simultaneously. Quantitative methods included pop-up questions administered during the live webinars, surveys administered following webinars, and an analysis of YouTube analytics. Qualitative methods included semistructured interviews with persons with SCI and health care providers who attended at least one webinar. The results were integrated, following methods adapted from Creswell and Clark. ResultsA total of 234 individuals attended at least 1 of the 6 webinars that took place during the 6-month study period. In total, 13.2% (31/234) of the participants completed the postwebinar survey, and 23% (7/31) participated in the semistructured interviews. The reach of the webinar series was mainly to persons with SCI, followed by health professionals, with most of them living in urban areas. The topics sexuality and research were the most viewed on YouTube. The knowledge disseminated during the webinars was mainly perceived as valid and useful, related to the fact that the presentation format involved people with lived experience and clinical experts. The webinars did not necessarily help build a new extended community of people involved in SCI but helped strengthen the existing community of people with SCI in Alberta. The webinar positively influenced the perceptions of normality and disability regarding people with SCI. The webinar format was perceived as highly usable and accessible. ConclusionsThe webinar series was associated with improved participant knowledge of what is possible to achieve after an SCI and their perceptions of disability. The long-term implementation of this initiative is feasible, but further considerations to increase its reach to rural areas and ensure the integration of diverse individuals should be taken.

Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1–4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= −0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

The neuropathological sequelae of stroke and subsequent recovery are incompletely understood. Here, we investigated the metabolic dynamics following stroke to advance the understanding of the pathophysiological mechanisms orchestrating stroke recovery. Using a nuclear magnetic resonance (NMR)-driven metabolomic profiling approach for urine samples obtained from a clinical group, the objective of this research was to (1) identify novel biomarkers indicative of severity and recovery following stroke, and (2) uncover the biochemical pathways underlying repair and functional recovery after stroke. Urine samples and clinical stroke assessments were collected during the acute (2–11 days) and chronic phases (6 months) of stroke. Using a 700 MHz 1H NMR spectrometer, metabolomic profiles were acquired followed by a combination of univariate and multivariate statistical analyses, along with biological pathway analysis and clinical correlations. The results revealed changes in phenylalanine, tyrosine, tryptophan, purine, and glycerophospholipid biosynthesis and metabolism during stroke recovery. Pseudouridine was associated with a change in post-stroke motor recovery. Thus, NMR-based metabolomics is able to provide novel insights into post-stroke cellular functions and establish a foundational framework for future investigations to develop targeted therapeutic interventions, advance stroke diagnosis and management, and enhance overall quality of life for individuals with stroke.

Scope: Early in the COVID-19 pandemic, community rehabilitation stakeholders from a provincial health system designed a novel telerehabilitation service. The service provided wayfinding and self-management advice to individuals with musculoskeletal concerns, neurological conditions, or post-COVID-19 recovery needs. This study evaluated the efficiency of the service in improving access to care. Methodology: We used multiple methods including secondary data analyses of call metrics, narrative analyses of clinical notes using artificial intelligence (AI) and machine learning (ML), and qualitative interviews. Conclusions: Interviews revealed that the telerehabilitation service had the potential to positively impact access to rehabilitation during the COVID-19 pandemic, for individuals living rurally, and for individuals on wait lists. Call metric analyses revealed that efficiency may be enhanced if call handling time was reduced. AI/ML analyses found that pain was the most frequently-mentioned keyword in clinical notes, suggesting an area for additional telerehabilitation resources to ensure efficiency.

IntroductionThe rising prevalence of complex chronic conditions and growing intricacies of healthcare systems emphasizes the need for interdisciplinary partnerships to advance coordination and quality of rehabilitation care. Registry databases are increasingly used for clinical monitoring and quality improvement (QI) of health system change. Currently, it is unclear how interdisciplinary partnerships can best mobilize registry data to support QI across care settings for complex chronic conditions.PurposeWe employed spinal cord injury (SCI) as a case study of a highly disruptive and debilitating complex chronic condition, with existing registry data that is underutilized for QI. We aimed to compare and converge evidence from previous reports and multi-disciplinary experts in order to outline the major elements of a strategy to effectively mobilize registry data for QI of care for complex chronic conditions.MethodsThis study used a convergent parallel-database variant mixed design, whereby findings from a systematic review and a qualitative exploration were analyzed independently and then simultaneously. The scoping review used a three-stage process to review 282 records, which resulted in 28 articles reviewed for analysis. Concurrent interviews were conducted with multidisciplinary-stakeholders, including leadership from condition-specific national registries, members of national SCI communities, leadership from SCI community organizations, and a person with lived experience of SCI. Descriptive analysis was used for the scoping review and qualitative description for stakeholder interviews.ResultsThere were 28 articles included in the scoping review and 11 multidisciplinary-stakeholders in the semi-structured interviews. The integration of the results allowed the identification of three key learnings to enhance the successful design and use of registry data to inform the planning and development of a QI initiative: enhance utility and reliability of registry data; form a steering committee lead by clinical champions; and design effective, feasible, and sustainable QI initiatives.ConclusionThis study highlights the importance of interdisciplinary partnerships to support QI of care for persons with complex conditions. It provides practical strategies to determine mutual priorities that promote implementation and sustained use of registry data to inform QI. Learnings from this work could enhance interdisciplinary collaboration to support QI of care for rehabilitation for persons with complex chronic conditions.

BackgroundSurveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs. ObjectiveThis study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes. MethodsPatients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model’s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score. ResultsData from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms. ConclusionsThe EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

Background: Analysis of fluid metabolites has the potential to provide insight into the neuropathophysiology of injury in patients with traumatic brain injury (TBI). Objective: Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined (1) if urinary metabolites change during recovery in patients with mild to severe TBI; (2) whether changes in urinary metabolites correlate to injury severity; (3) whether biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery. Methods: Urine samples were collected within 7 days and at 6-months post-injury in male participants (n = 8) with mild-severe TBI. Samples were analyzed with NMR-based quantitative spectroscopy for metabolomic profiles and analyzed with multivariate statistical and machine learning-based analyses. Results: Lower levels of homovanillate (R = −0.74, p ≤ 0.001), L-methionine (R = −0.78, p

Introduction Very little is known about how the COVID-19 pandemic has affected the health of residents and the healthcare system in Alberta, Canada. The purpose of this study is to establish an observational study to characterise the health of residents in Alberta, Canada, over time, covering a population that tested negative or positive for COVID-19 during the pandemic. The primary outcome is to characterise ‘long COVID-19’ and the health status of residents during the COVID-19 pandemic. Secondary outcomes include the estimation of the risk of and risk factors associated with adverse health outcomes and healthcare utilisation and burdens.Methods and analysis This is a population-level provincial observational study which will follow-up with Alberta residents who underwent testing for COVID-19 and completed surveys adapted from the ISARIC COVID-19 long-term follow-up survey. The survey data will be linked with medical records. Statistical analyses will be carried out to characterise ‘long COVID-19’ and the health status of residents during the pandemic. The outcomes of this study will inform strategies for primary care and rehabilitation services to prevent chronic consequences; contribute to healthcare management, interventional studies, rehabilitation and health management to reduce overall morbidity and improve long-term outcomes of COVID-19 and the COVID-19 pandemic and potentially guide a self-evaluation of a remote monitoring system to manage individuals’ health.Ethics and dissemination This study was reviewed and approved by the University of Alberta ethics committee (Study ID: Pro00112053 & Pro00113039) on 13 August 2021 and adheres to the Alberta Health Services research information management policy. Study results will be used to manage clinical care, published in peer-reviewed journals and presented at local, national and international conferences.Protocol version 6 June 2022EuroQol ID 161 015.

One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.

Current assessments of recovery following spinal cord injury (SCI) focus on clinical outcome measures. These assessments bear an inherent risk of bias, emphasizing the need for more reliable prognostic biomarkers to measure SCI severity. This study evaluated fluid biomarkers as an objective tool to aid with prognosticating outcomes following SCI. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolomics approach of urine samples, the objectives were to determine (a) if alterations in metabolic profiles reflect the extent of recovery of individual SCI patients, (b) whether changes in urine metabolites correlate to patient outcomes, and (c) whether biological pathway analysis reflects mechanisms of neural damage and repair. An inception cohort exploratory pilot study collected morning urine samples from male SCI patients (n=6) following injury and again at 6-months post-injury. A 700 MHz Bruker Avance III HD NMR spectrometer was used to acquire the metabolic signatures of urine samples, which were used to derive metabolic pathways. Multivariate statistical analyses were used to identify changes in metabolic signatures, which were correlated to clinical outcomes in the Spinal Cord Independence Measure (SCIM). Among SCI-induced metabolic changes, biomarkers which significantly correlated to patient SCIM scores included caffeine (R = -0.76, p

Introduction A novel telerehabilitation service provides wayfinding and self-management advice to persons with neurological, musculoskeletal, or coronavirus disease 2019 related rehabilitation needs. Method We utilized multiple methods to evaluate the impact of the service. Surveys clarified health outcomes (quality of life, self-efficacy, social support) and patient experience (telehealth usability; general experience) 3-months post-call. We analysed associations between, and within, demographics and survey responses. Secondary analyses described health care utilization during the first 6 months. Results Sixty-eight callers completed the survey (42% response rate). Self-efficacy was significantly related to quality of life, interpersonal support and becoming productive quickly using the service. Becoming productive quickly was significantly related to quality of life. Education level was related to ethnicity. Survey respondents’ satisfaction and whether they followed the therapist's recommendations were not significantly associated with demographics. Administrative data indicated there were 124 callers who visited the emergency department before, on, or after their call. The average (SD) frequency of emergency department visits before was 1.298 times (1.799) compared to 0.863 times (1.428) after. Discussion This study offers insights into the potential impact of the telerehabilitation service amidst pandemic restrictions. Usability measurements showed that callers were satisfied, corroborating literature from pre-pandemic contexts. The satisfaction and acceptability of the service does not supplant preferences for in-person visits. The survey sample reported lower quality of life compared with the provincial population, conflicting with pre-pandemic research. Findings may be due to added stressors associated with the pandemic. Future research should include population-level comparators to better clarify impact.

The assessment, management, and prognostication of spinal cord injury (SCI) mainly rely upon observer-based ordinal scales measures. 1H nuclear magnetic resonance (NMR) spectroscopy provides an effective approach for the discovery of objective biomarkers from biofluids. These biomarkers have the potential to aid in understanding recovery following SCI. This proof-of-principle study determined: (a) If temporal changes in blood metabolites reflect the extent of recovery following SCI; (b) whether changes in blood-derived metabolites serve as prognostic indicators of patient outcomes based on the spinal cord independence measure (SCIM); and (c) whether metabolic pathways involved in recovery processes may provide insights into mechanisms that mediate neural damage and repair. Morning blood samples were collected from male complete and incomplete SCI patients (n = 7) following injury and at 6 months post-injury. Multivariate analyses were used to identify changes in serum metabolic profiles and were correlated to clinical outcomes. Specifically, acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid significantly related to SCIM scores. These preliminary findings suggest that specific metabolites may serve as proxy measures of the SCI phenotype and prognostic markers of recovery. Thus, serum metabolite analysis combined with machine learning holds promise in understanding the physiology of SCI and aiding in prognosticating outcomes following injury.

Introduction Despite the availability of various pressure injury (PI) prevention strategies (e.g., risk identification, use of pressure re-distribution surfaces, frequent repositioning), they persist as a significant issue for healthcare systems worldwide. Continuous pressure imaging (CPI) is a novel technology that could be integrated within a comprehensive approach to the prevention of PIs. We studied the perceptions of healthcare providers and patients/families to identify facilitators and barriers to the use of this technology. Methods Hospitalized patients/family members from a randomized controlled trial assessing the efficacy of CPI in preventing PIs completed a survey after 72 hours (or upon discharge from hospital) of CPI monitoring. They were asked questions about prior and current experience with CPI technology. For healthcare providers, perceptions on the use of the device and its impact on care were explored through a survey distributed by email or hard copies. Results A total of 125 healthcare providers and 525 patients/family members completed the surveys. Of the healthcare providers, 95% either agreed/strongly agreed that the CPI technology was easy to use and 65% stated that the device improved how they provided pressure relief for patients. Identified issues with the device were cost, the fitting of the mattress cover, and the fixation of the patients/families on the device. Over a quarter of the patient/family respondents agreed/strongly agreed that the device influenced how pressure relief was provided. This response was statistically associated with whether the monitor was turned on (intervention arm; 52.7%) or off (control arm; 4.2%). Discussion and conclusion CPI technology was positively perceived by healthcare providers. Most patients/families felt it influenced care when the CPI monitor was turned on. Concerns raised around cost and the ease of use of these devices by healthcare providers may affect the decisions of healthcare system administrators to adopt and implement this technology.

BackgroundThe COVID-19 pandemic and concomitant governmental responses have created the need for innovative and collaborative approaches to deliver services, especially for populations that have been inequitably affected. In Alberta, Canada, two novel approaches were created in Spring 2020 to remotely support patients with complex neurological conditions and rehabilitation needs. The first approach is a telehealth service that provides wayfinding and self-management advice to Albertans with physical concerns related to existing neurological or musculoskeletal conditions or post-COVID-19 recovery needs. The second approach is a webinar series aimed at supporting self-management and social connectedness of individuals living with spinal cord injury. ObjectiveThe study aims to evaluate the short- and long-term impacts and sustainability of two virtual modalities (telehealth initiative called Rehabilitation Advice Line [RAL] and webinar series called Alberta Spinal Cord Injury Community Interactive Learning Seminars [AB-SCILS]) aimed at advancing self-management, connectedness, and rehabilitation needs during the COVID-19 pandemic and beyond. MethodsWe will use a mixed-methods evaluation approach. Evaluation of the approaches will include one-on-one semistructured interviews and surveys. The evaluation of the telehealth initiative will include secondary data analyses and analysis of call data using artificial intelligence. The evaluation of the webinar series will include analysis of poll questions collected during the webinars and YouTube analytics data. ResultsThe proposed study describes unique pandemic virtual modalities and our approaches to evaluating them to ensure effectiveness and sustainability. Implementing and evaluating these virtual modalities synchronously allows for the building of knowledge on the complementarity of these methods. At the time of submission, we have completed qualitative and quantitative data collection for the telehealth evaluation. For the webinar series, so far, we have distributed the evaluation survey following three webinars and have conducted five attendee interviews. ConclusionsUnderstanding the impact and sustainability of the proposed telehealth modalities is important. The results of the evaluation will provide data that can be actioned and serve to improve other telehealth modalities in the future, since health systems need this information to make decisions on resource allocation, especially in an uncertain pandemic climate. Evaluating the RAL and AB-SCILS to ensure their effectiveness demonstrates that Alberta Health Services and the health system care about ensuring the best practice even after a shift to primarily virtual care. International Registered Report Identifier (IRRID)DERR1-10.2196/28267

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided.

Introduction The Spinal Cord Injury (SCI) population consists of two main sub-groups: traumatic (TSCI) and non-traumatic (NTSCI). TSCI has been studied; however less attention has been given to NTSCI. It is important to understand both SCI sub-groups for identification of knowledge gaps and subsequent health service planning. Objectives and Approach The goal is to study the SCI population (both TSCI and NTSCI) in Alberta, Canada, leveraging recent administrative health data. It is difficult to identify NTSCI patients for their heterogeneous conditions, and relatively low prevalence. Consequently, we followed a validated algorithm using particular ICD-10-CA codes, to identify (and index) adult SCI patients from Ambulatory and Inpatient records between April 1, 2006 and March 31, 2016. Indexed patients were linked to various databases (inpatient, ambulatory, physician claims, provincial insurance registry), and analyzed in multiple perspectives such as demographics patterns, deaths, resource and cost utilization, geographic distribution, and care equity between groups. Results Through 10 years of data we have identified 5217 SCI patients (3309 TSCI; 1908 NTSCI). 68.7% TSCI and 58.6% NTSCI are male. NTSCI patients are approximately 10 years older (46.3 TSCI; 54.5 NTSCI), and have a 3-point higher Charlson score. 1-year mortality in NTSCI is approximately 2.4 times the TSCI group. Hospitalizations, ER visits, critical care time have also been examined. Patients with NTSCI had a higher median index LOS (14 days IQR (4-51)) compared to the traumatic group who had much higher variability (11 days IQR (11-65.5)). Noted 13.7% NTSCI patients and 19.5% TSCI do not have hospitalizations after index (a diverse characteristic of SCI). Resource Intensity Weights, physician billing, rural-urban area utilization have also been compared between the sub-groups. Conclusion/Implications With the use of administrative databases and a validated algorithm, we described a diverse patient cohort with two main sub-groups (TSCI/NTSCI). Both groups were analyzed upon multiple topics and showed variations. Our results have provided updated knowledge of a comprehensive SCI population in Alberta, Canada, and may lead to improvements on care-giving model.

Introduction Over 4.5 TB of data will be collected in a randomized clinical trial assessing continuous pressure imaging technology in reducing pressure injuries. To permit efficient analysis we examined different approaches to generating a reduced representation of the data that still produced similar analytical results to the full data set. Objectives and Approach Our primary objective was to develop an approach for reducing the pressure data to a manageable size, without loss of information. Frame by frame analysis of excluded participant imaging data allows us to identify changes in patient position. We explored different sampling frequencies (q5, 30, 60, 120, \& 240 seconds) for analysis, coalesced frames with periods of stillness, and used correlation coefficients as a measure of similarity between adjacent frames to identify patient position changes. Heat maps and plots were used to evaluate the performance characteristics and usefulness of different sampling frequencies and correlation coefficients to compress the data. Results A sampling frequency q60 seconds provided reasonable representation of changes in interface pressure over time. This translates to using only 1.7\% of the collected data in our analyses. When the threshold of correlation coefficient was set at 0.99 for coalescing the information based on position changes, then there was a maximum of 80 position changes detected. Therefore, approximately 160 frames q24 hours is sufficient to represent pressure states of participants at high risk of developing pressure injuries. In total we would require 480 frames (160*3) from 72 hours of collected data for our analyses without loss of position changes and pressure information. This represents approximately 0.185\% of the data collected. Conclusion/Implications Decreasing the sampling frequency significantly reduced our data size without compromising resolution, while the use of correlation coefficients was effective at coalescing the continuous pressure imaging data. These methods of data reduction may be applicable to pre-processing large datasets obtained from other device monitoring technologies.