Effect of psychological interventions on depressive symptoms in long-term rehabilitation after an acquired brain injury: a systematic review and meta-analysis

OBJECTIVE To summarize empirical studies on the effectiveness of psychological interventions in long-term rehabilitation after an acquired brain injury (ABI) in reducing depressive symptoms. DATA SOURCES A systematic literature search was conducted on MEDLINE, PsycINFO, Embase, and CINAHL to identify articles published between January 1990 and October 2011. Search terms included the 3 concepts (1) "brain injur*" or "stroke," (2) "psychotherap*" or "therapy" or "intervention" or "rehabilitation," and (3) "depress*." STUDY SELECTION Studies evaluating psychological interventions in patients after ABI were included. Time since injury was on average more than 1 year. Trials reported data on validated depression questionnaires before and after the psychological intervention. DATA EXTRACTION Two independent reviewers extracted information from the sample, the intervention, and the outcome of the included studies and calculated effect sizes (ESs) from depression questionnaires. Thirteen studies were included in a pre-post analysis. Seven studies were eligible for a meta-analysis of ESs in active interventions and control conditions. DATA SYNTHESIS Pre-post ESs were significant in 4 of 13 studies. The overall ES of .69 (95% confidence interval [CI], .29-1.09) suggests a medium effectiveness of psychological interventions on depressive symptoms compared with control conditions. Moderator analysis of the number of sessions and adequate randomization procedure did not show significant ES differences between strata. Studies with adequate randomization did not, however, suggest the effectiveness of psychological interventions on depressive symptoms after ABI. CONCLUSIONS Psychological interventions are a promising treatment option for depressive symptoms in long-term rehabilitation after ABI. Since only a few adequately randomized controlled trials (RCTs) exist, more RCTs are required to confirm this initial finding.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Modelling ecological cognitive rehabilitation therapies for building virtual environments in brain injury

Brain Injury (BI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as virtual reality or interactive video allow developing rehabilitation therapies based on reproducible Activities of Daily Living (ADLs), increasing the ecological validity of the therapy. However, the lack of frameworks to formalize and represent the definition of this kind of therapies can be a barrier for widespread use of interactive virtual environments in clinical routine.

Equity in access to outpatient rehabilitation services for children with traumatic brain injury and public insurance

Thesis (Master's)--University of Washington, 2016-06

Referral to rehabilitation following traumatic brain injury: a model for understanding inequities in access

Identifying inequities in access to health care requires critical scrutiny of the patterns and processes of care decisions. This paper describes a conceptual model. derived from social problems theory. which is proposed as a useful framework for explaining patterns of post-acute care referral and in particular, individual variations in referral to rehabilitation after traumatic brain injury (TBI). The model is based on three main components: (1) characteristics of the individual with TBI, (2) activities of health care professionals and the processes of referral. and (3) the contexts of care. The central argument is that access to rehabilitation following TBI is a dynamic phenomenon concerning the interpretations and negotiations of health care professionals. which in turn are shaped by the organisational and broader health care contexts. The model developed in this paper provides opportunity to develop a complex analysis of post-acute care referral based on patient factors, contextual factors and decision-making processes. It is anticipated that this framework will have utility in other areas examining and understanding patterns of access to health care. (C) 2002 Elsevier Science Ltd. All rights reserved.

Prospective memory rehabilitation for adults with traumatic brain injury: A compensatory training programme

Primary objective: To describe a prospective memory rehabilitation programme based on a compensatory training approach and report the results of three case studies. Research design: Programme evaluation using pre-and post-intervention assessments and telephone follow-up. Methods and procedures: Three participants with traumatic brain injury completed 8 weeks of training with 1 - 2 hour individual sessions. Assessments were formal prospective memory assessment, self-report and measures of diary use. Experimental interventions: Intervention aimed to identify potential barriers, establish self-awareness of memory deficits, introduce a customized compensatory tool, a cueing system and organizational strategies. A significant other was involved in training to assist generalization. Main outcomes and results: All three participants improved on formal prospective memory assessment and demonstrated successful diary use after the programme. Self-report of prospective memory failure fluctuated and may reflect increased self-awareness. Conclusion: A compensatory approach may be useful in improving prospective memory performance following TBI.

Measurement of functional ability following traumatic brain injury using the Clinical Outcomes Variable Scale: A reliability study

This study determined the inter-tester and intra-tester reliability of physiotherapists measuring functional motor ability of traumatic brain injury clients using the Clinical Outcomes Variable Scale (COVS). To test inter-tester reliability, 14 physiotherapists scored the ability of 16 videotaped patients to execute the items that comprise the COVS. Intra-tester reliability was determined by four physiotherapists repeating their assessments after one week, and three months later. The intra-class correlation coefficients (ICC) were very high for both inter-tester reliability (ICC > 0.97 for total COVS scores, ICC > 0.93 for individual COVS items) and intra-tester reliability (ICC > 0.97). This study demonstrates that physiotherapists are reliable in the administration of the COVS.

A longitudinal study of self-awareness: Functional deficits underestimated by persons with brain injury

A longitudinal study of 55 adults with severe traumatic brain injury (TBI) investigated the areas of function for which they lacked self-awareness of their level of competency. Data were collected at 3 and 12 months post-injury using the Patient Competency Rating Scale. Self-awareness was measured by comparing patient self-ratings with the ratings of an infor mant. The results were consistent with previous studies, indicating that self-awareness was most impaired for activities with a large cognitive and socioemotional component, and least impaired for basic activities of daily living, memory activities, and overt emotional responses. For most areas of function that were overestimated at 3 months post-injury, self-awareness subsequently improved during the first year after injury.

Cluster analysis of self-awareness levels in adults with traumatic brain injury and relationship to outcome

The purpose of this study was to investigate the relationship between self-awareness, emotional distress, motivation, and outcome in adults with severe traumatic brain injury. A sample of 55 patients were selected from 120 consecutive patients with severe traumatic brain injury admitted to the rehabilitation unit of a large metropolitan public hospital. Subjects received multidisciplinary inpatient rehabilitation and different types of outpatient rehabilitation and community-based services according to availability and need, Measures used in the cluster analysis were the Patient Competency Rating Scale, Self-Awareness of Deficits Interview, Head Injury Behavior Scale, Change Assessment Questionnaire, the Beck Depression Inventory, and Beck Anxiety Inventory; outcome measures were the Disability Rating Scale, Community Integration Questionnaire, and Sickness Impact Profile. A three-cluster solution was selected, with groups labeled as high self-awareness (n = 23), low self-awareness (n = 23), and good recovery (n = 8). The high self-awareness cluster had significantly higher levels of self-awareness, motivation, and emotional distress than the low self-awareness cluster but did not differ significantly in outcome. Self-awareness after brain injury is associated with greater motivation to change behavior and higher levels of depression and anxiety; however, it was not clear that this heightened motivation actually led to any improvement in outcome. Rehabilitation timing and approach may need to be tailored to match the individual's level of self-awareness, motivation, and emotional distress.