OBJECTIVES: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. METHODS: Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. RESULTS: Latent class analysis categorized participants into "high" (44%) and "low" (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. CONCLUSIONS: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038-1049).
OBJECTIVES: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. METHODS:Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. RESULTS: Latent class analysis categorized participants into "high" (44%) and "low" (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. CONCLUSIONS: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038-1049).
Authors: Moses K Nyongesa; Derrick Ssewanyana; Agnes M Mutua; Esther Chongwo; Gaia Scerif; Charles R J C Newton; Amina Abubakar Journal: Front Psychol Date: 2019-03-01
Authors: Angela Lumba-Brown; Jamshid Ghajar; Jordan Cornwell; O Josh Bloom; James Chesnutt; James R Clugston; Raina Kolluri; John J Leddy; Masaru Teramoto; Gerard Gioia Journal: Concussion Date: 2019-11-01
Authors: Mekala Neelakantan; Brinda Ryali; Maria Demma Cabral; Ann Harris; Juli McCarroll; Dilip R Patel Journal: Int J Environ Res Public Health Date: 2020-10-19 Impact factor: 3.390