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BackgroundThe World Health Organization’s International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing “context” as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them. ObjectiveThe objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities. MethodsReddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde’s updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation. ResultsWe analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast – 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps “to know accessibility before you go” to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was “resilience,” recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the “resilience” definition. ConclusionsThis study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.

BackgroundTraumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments. ObjectiveThe aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks. MethodsThis study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate. ResultsWe analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P

Most research focuses around impairments in body function and structure, with relatively only a small number exploring their social impact. 1) compare characteristics for individuals who before stroke were blue collar vs. white collar workers 2) identify clinical, functional, and job-related factors associated with return to work within 1 year after discharge 3) identify specific ADL individual items (assessed at rehabilitation discharge) as return to work predictors and 4) identify return to work causal mediators. Retrospective observational cohort study, analyzing adult patients with stroke admitted to rehabilitation between 2007 and 2021, including baseline Barthel Index (BI) and return to work assessments between 2008 and 2022. Kaplan–Meier survival curves and Cox proportional hazards were applied. Causal mediation analyses using 1000-bootstrapped simulations were performed. A total of 802 individuals were included (14.6% returned to work), 53.6% blue-collar and 46.4% white-collar. Blue-collar workers showed significantly higher proportion of ischemic stroke, diabetes, dyslipidemia, and hypertension. Individuals not returning to work presented a higher proportion of blue collar, dominant side affected, aphasia, lower BI scores, and larger length of stay (LOS). Multivariable Cox proportional hazards identified age at injury, aphasia, hypertension, and total discharge BI score (C-Index = 0.74). Univariable Cox models identified three independent BI items at all levels of independence: bathing (C-Index = 0.58), grooming (C-Index = 0.56) and feeding (C-Index = 0.59). BI efficiency (gain/LOS) was a causal mediator. Blue collar workers showed higher proportion of risk factors and comorbidities. Novel factors, predictors, and a return to work mediator were identified. [ABSTRACT FROM AUTHOR]

Introduction: Even in nonpandemic times, persons with disabilities experience emotional and behavioral disturbances which are distressing for them and for their close persons. We aimed at comparing the levels of stress in emotional and behavioral aspects, before and during coronavirus disease 2019 (COVID‐19), as reported by informal family caregivers of individuals with chronic traumatic brain injury (TBI) or stroke living in the community, considering two different stratifications of the recipients of care (cause and injury severity). Methods: We conducted a STROBE‐compliant prospective observational study analyzing informal caregivers of individuals with stroke (IC‐STROKE) or traumatic brain injury (IC‐TBI). IC‐STROKE and IC‐TBI were assessed in‐person before and during COVID‐19 online, using the Head Injury Behavior Scale (HIBS). The HIBS comprises behavioral and emotional subtotals (10 items each) and a total‐HIBS. Comparisons were performed using the McNemar's test, Wilcoxon signed‐rank test or t‐test. Recipients of care were stratified according to their injury severity using the National Institutes of Health Stroke Scale (NIHSS) and the Glasgow Coma Scale (GCS). Results: One hundred twenty‐two informal caregivers (62.3% IC‐STROKE and 37.7% IC‐TBI) were assessed online between June 2020 and April 2021 and compared to their own assessments performed in‐person 1.74 ± 0.88 years before the COVID‐19 lockdown. IC‐STROKE significantly increased their level of stress during COVID‐19 in five emotional items (impatience, frequent complaining, often disputes topics, mood change and overly sensitive) and in one behavioral item (overly dependent). IC‐TBI stress level only increased in one behavioral item (impulsivity). By injury severity, (i) mild (14.7%) showed no significant differences in emotional and behavioral either total‐HIBS (ii) moderate (28.7%) showed significant emotional differences in two items (frequent complaining and mood change) and (iii) severe (56.6%) showed significant differences in emotional (often disputes topics) and behavioral (impulsivity) items. Conclusions: Our results suggest specific items in which informal caregivers could be supported considering cause or severity of the recipients of care. [ABSTRACT FROM AUTHOR]