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Clinical factors associated with the development of nonuse learned after stroke: a prospective study.

Rafael Dalle Molle da CostaGustavo José LuvizuttoLaís Geronutti MartinsJuli Thomaz De SouzaTaís Regina da SilvaLorena Cristina Alvarez SartorFernanda Cristina WincklerGabriel Pinheiro ModoloEvelin Roberta Da Silva Dalle MolleSarah M Dos AnjosSilméia Garcia Zanati BazanLuis Cuadrado MartinRodrigo Bazan
Published in: Topics in stroke rehabilitation (2019)
Background: Upper extremity impairment is present in most of people with stroke. The use of the affected upper extremity can be impacted not only by physical impairment but also by abehavioral phenomenon called learned nonuse. Objective: The aim of this study was to evaluate which clinical factors in the acute phase are associated with the development of learned nonuse in the upper extremity after stroke. Methods: This cohort study included 38 patients with ischemic stroke. Hospital discharge data were collected for clinical aspects, scales of severity, incapacity and autonomy, as well as for neuromuscular and sensory evaluations. At 90 days after hospital discharge, the score on the Motor Activity Log scale for detecting learned nonuse was obtained, and life quality was evaluated by the EuroQol. The individuals with and without learned nonuse were compared by attest for univariate analysis, and ageneralized linear model was employed to find possible predictors, which were considered significant p <0.05. Results: In the statistical model, age (p= .006), severity at discharge (p= .036), handgrip strength (p= .000), altered sensitivity (p= .011), incapacity at discharge (p= .009) and autonomy at discharge (p= .011) were found to be associated with learned nonuse. In relation to quality of life, mobility, personal care, usual activities, anxiety, depression and perception had lower mean values in the learned nonuse group. Conclusion: Age, severity of stroke, incapacity and neuromuscular and sensory compromises are associated with upper extremity learned nonuse in stroke patients.
Keyphrases
  • atrial fibrillation
  • healthcare
  • mental health
  • sleep quality
  • depressive symptoms
  • machine learning
  • data analysis