Time from injury and age interact in relationship with perceived quality of life outcomes following vocation-focused neuropsychological rehabilitation.

At the group level, community-based neuropsychological rehabilitation interventions with a vocational focus are generally effective among individuals with brain injuries. However, individual participants vary significantly in the extent of their improvement, prompting attempts to elucidate individual, injury-related, and environmental factors affecting prognosis. In this study, we examined the relationships between one such factor - "time from injury" (the time between injury and intervention) - and two outcome measures: employment status and perceived quality of life (PQoL), in 157 brain injury survivors, before and after a holistic neuropsychological vocational rehabilitation program. We also examined whether relationships between the variables were moderated by age at onset of treatment and injury severity. In the entire sample, both the proportion of employed participants and average PQoL increased following program participation. Neither, time from injury, severity, nor age at onset of treatment predicted the increase in employment proportion, and severity was not a significant predictor of PQoL. However, an interactive effect indicated that when treatment was started at a younger age, longer time from injury predicted higher levels of PQoL, but when treatment was started at older ages, longer time from injury predicted lower levels of PQoL. When interpreted alongside existing literature, these results suggest that delaying vocational components of rehabilitation can be beneficial for younger participants, while the effectiveness of vocational rehabilitation can be maximized by starting as early as possible among older participants. Most importantly, regardless of age, it appears that vocational rehabilitation can be effective even when initiated many years after injury.

Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders.

INTRODUCTION: We developed and validated an abbreviated Digital Sleep Questionnaire (DSQ) to identify common societal sleep disturbances including insomnia, delayed sleep phase syndrome (DSPS), insufficient sleep syndrome (ISS), and risk for obstructive sleep apnea (OSA). METHODS: The DSQ was administered to 3799 community volunteers, of which 2113 were eligible and consented to the study. Of those, 247 were interviewed by expert sleep physicians, who diagnosed ≤2 sleep disorders. Machine Learning (ML) trained and validated separate models for each diagnosis. Regularized linear models generated 15-200 features to optimize diagnostic prediction. Models were trained with five-fold cross-validation (repeated five times), followed by robust validation testing. ElasticNet models were used to classify true positives and negatives; bootstrapping optimized probability thresholds to generate sensitivities, specificities, accuracies, and area under the receiver operating curve (AUC). RESULTS: Compared to reference subgroups, physician-diagnosed sleep disorders were marked by DSQ evidence of sleeplessness (insomnia, DSPS, OSA), sleep debt (DSPS, ISS), airway obstruction during sleep (OSA), blunted circadian variability in alertness (DSPS), sleepiness (DSPS and ISS), increased alertness (insomnia) and global impairment in sleep-related quality of life (all sleep disorders). ElasticNet models validated each diagnosis with high sensitivity (80-83%), acceptable specificity (63-69%), high AUC (0.80-0.85) and good accuracy (agreement with physician diagnoses, 68-73%). DISCUSSION: A brief DSQ readily engaged and efficiently screened a large population for common sleep disorders. Powered by ML, the DSQ can accurately classify sleep disturbances, demonstrating the potential for improving the sleep, health, productivity and safety of populations.

Self-efficacy and acceptance of disability following mild traumatic brain injury: A pilot study.

Recovery from mild traumatic brain injury (mTBI) and regaining emotional equilibrium afterward can take much longer than the typical three months. Recent works attribute persisting complaints to psychological factors, primarily the negative perception of mTBI. However, research has yet to demonstrate how self-beliefs concerning capability are linked to perception and ability to accept injury. The objective of this study was to investigate how perceived general self-efficacy (GSE) and acceptance of disability (AD) relate to emotional outcome following mTBI. Thirty individuals aged 21-57, all of whom were at least three-month post diagnosis of mTBI, underwent a psychiatric clinical interview assessing depression, post-traumatic stress disorder (PTSD), and quality of life (QoL), and completed self-report scales to evaluate psychological factors. Scores on AD, depression, and QoL for most life domains were significantly lower and worse than among the normative population. Pearson coefficients indicated significant correlations between psychological factors and emotional outcome. Mediation analysis showed a significant role of AD in mediating the correlation between GSE and depression/general QoL, irrespective of PTSD. Low self-efficacy accentuates negative perception of the injury which, in turn, leads to poor emotional outcomes post mTBI. Therefore, AD should become the focus of therapeutic interventions.