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- All Subjects: systematic review
- Creators: Bosch, Pamela
Description
Background and Purpose— There is limited conclusive data on both pharmacological and holistic treatment options to improve cognition in adults after stroke. In particular, there is lacking evidence for cognitive rehabilitation in the subacute and chronic phases when cognitive impairment may be more perceptible. In this meta-analytic review, our primary objective was to determine the cognitive effects of aerobic exercise on post-stroke adults in the post-acute phases. Secondary objectives were to investigate the differential effects of aerobic exercise on sub-domains of cognitive function.
Methods— Data were extracted and filtered from electronic databases PubMed (MEDLINE), CINAHL, Embase, PsycINFO, and Scopus. Intervention effects were represented by Hedges’ g and combined into pooled effect sizes using random effects models. Heterogeneity was evaluated using the Chi-squared (Q) and I-squared statistics.
Results— Five studies met inclusion criteria, representing data from 182 participants. The primary analysis produced a positive overall effect of aerobic exercise on cognitive performance (Hedges’ g [95% confidence interval]= 0.42 [0.007–0.77]). Effects were significantly different from zero for aerobic interventions combined with other physical activity interventions (Hedges’ g [CI] =0.59 [0.26 to 0.92]), but not for aerobic interventions alone (P= 0.40). In specific subdomains, positive moderate effects were found for global cognitive function (Hedges’ g [CI] =0.79 [0.31 to 1.26]) but not for attention and processing speed (P=0.08), executive function (P= 0.84), and working memory (P=0.92).
Conclusions— We determined that aerobic exercise combined with other modes of training produced a significant positive effect on cognition in adults after stroke in the subacute and chronic phases. Our analysis supports the use of combined training as a treatment option to enhance long-term cognitive function in adults after stroke. Further research is needed to determine the efficacy of aerobic training alone.
Methods— Data were extracted and filtered from electronic databases PubMed (MEDLINE), CINAHL, Embase, PsycINFO, and Scopus. Intervention effects were represented by Hedges’ g and combined into pooled effect sizes using random effects models. Heterogeneity was evaluated using the Chi-squared (Q) and I-squared statistics.
Results— Five studies met inclusion criteria, representing data from 182 participants. The primary analysis produced a positive overall effect of aerobic exercise on cognitive performance (Hedges’ g [95% confidence interval]= 0.42 [0.007–0.77]). Effects were significantly different from zero for aerobic interventions combined with other physical activity interventions (Hedges’ g [CI] =0.59 [0.26 to 0.92]), but not for aerobic interventions alone (P= 0.40). In specific subdomains, positive moderate effects were found for global cognitive function (Hedges’ g [CI] =0.79 [0.31 to 1.26]) but not for attention and processing speed (P=0.08), executive function (P= 0.84), and working memory (P=0.92).
Conclusions— We determined that aerobic exercise combined with other modes of training produced a significant positive effect on cognition in adults after stroke in the subacute and chronic phases. Our analysis supports the use of combined training as a treatment option to enhance long-term cognitive function in adults after stroke. Further research is needed to determine the efficacy of aerobic training alone.
ContributorsMitchell, Michaela (Author) / Holzapfel, Simon (Thesis director) / Bosch, Pamela (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Systematic Reviews (SRs) aim to synthesize the totality of evidence for clinical practice and are important in making clinical practice guidelines and health policy decisions. However, conducting SRs manually is a laborious and time-consuming process. This challenge is growing due to the increase in the number of databases to search and the papers being published. Hence, the automation of SRs is an essential task. The goal of this thesis work is to develop Natural Language Processing (NLP)-based classifiers to automate the title and abstract-based screening for clinical SRs based on inclusion/exclusion criteria. In clinical SRs, a high-sensitivity system is a key requirement. Most existing methods for SRs use binary classification systems trained on labeled data to predict inclusion/exclusion. While previous studies have shown that NLP-based classification methods can automate title and abstract-based screening for SRs, methods for achieving high-sensitivity have not been empirically studied. In addition, the training strategy for binary classification has several limitations: (1) it ignores the inclusion/exclusion criteria, (2) lacks generalization ability, (3) suffers from low resource data, and (4) fails to achieve reasonable precision at high-sensitivity levels. This thesis work presents contributions to several aspects of the clinical systematic review domain. First, it presents an empirical study of NLP-based supervised text classification and high-sensitivity methods on datasets developed from six different SRs in the clinical domain. Second, this thesis work provides a novel approach to view SR as a Question Answering (QA) problem in order to overcome the limitations of the binary classification training strategy; and propose a more general abstract screening model for different SRs. Finally, this work provides a new QA-based dataset for six different SRs which is made available to the community.
ContributorsParmar, Mihir Prafullsinh (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Thesis advisor) / Riaz, Irbaz B (Committee member) / Arizona State University (Publisher)
Created2021