Fatores preditores de desempenho cognitivo de idosos brasileiros em teste de Stroop
Data
2020
Autores
Braga, Pedro Luiz Garcia 
Orientadores
Silva, Sergio Gomes da
Tipo
Tese de doutorado
Título da Revista
ISSN da Revista
Título de Volume
Resumo
Objetivo: No presente estudo, analisamos quais variáveis físicas e funcionais constituem-se como fatores preditores de desempenho cognitivo em uma amostra de 498 idosos brasileiros (67,26% do sexo feminino). Método: Para tanto, utilizamos o teste de Stroop como ferramenta para avaliar o desempenho cognitivo e o Índice Geral de Aptidão Funcional (IGAF) para avaliar a aptidão funcional dos participantes. Resultado: A análise de regressão linear evidencia que sexo, precisamente o feminino (β= - 0,097; t= - 2,286; p=0,023), idade (β= 0,205; t= 4,606; p< 0,0001), escolaridade (β= - 0,280; t= - 6,358; p< 0,0001) e o IGAF (β= - 0,101; t= - 2,347; p< 0,02), foram fatores preditores do desempenho cognitivo. O índice de massa corporal (kg/m2) e o estado nutricional (baixo peso, eutrófico, sobrepeso ou obeso) não foram preditores significativos associados ao desempenho cognitivo dos idosos. Conclusões: Tomados em conjunto, nossos achados mostram que: (1) mulheres executam o teste de Stroop em menor tempo do que homens; (2) quanto mais velho o idoso, pior seu desempenho cognitivo no teste; (3) quanto maior a escolaridade dos idosos, melhor o tempo de execução do teste; (4) pontuações mais altas no IGAF levam a um melhor desempenho no teste de Stroop. Ademais, dentre as tarefas do IGAF, observou-se que a força muscular é fator preditor na execução do Teste de Stroop, tanto nos membros superiores quanto inferiores (flexão do cotovelo: β= - 0,201; t= - 4,672; p< 0,0001; sentar e levantar na cadeira: β= - 0,125; t= - 2,580; p< 0,01). Portanto, sexo, idade, escolaridade, aptidão funcional e força muscular são preditores de desempenho cognitivo em idosos.
Objective: To unravel further evidences of age-related cognitive decline, we analyzed which physical and functional variables are predictors of cognitive performance in a sample of 498 Brazilian elderly (67.26% female). Methods: To do so, we used the Stroop test as a tool to evaluate executive functions and the general functional fitness index (GFFI) to evaluate the functional fitness of the participants. Results: A linear regression analysis reveals that gender, precisely female (β= - 0.097; t= - 2.286; p=0.023), age (β= 0.205; t= 4.606; p< 0.0001), education (β= - 0.280; t= - 6.358; p< 0.0001) and GFFI (β= - 0.101; t= - 2.347; p< 0.02) were predictor factors of cognitive performance. Body mass index (kg/m2) and nutritional status (underweight, eutrophic, overweight, or obese) were not predictors of cognitive performance of elderlies. Interestingly, among the GFFI tasks, it was noted a muscle power influence in the execution test time, both in upper and lower limbs (elbow flexion: β= - 0.201; t= - 4.672; p< 0.0001; sit-to-stand: β= - 0.125; t= - 2.580; p< 0.01). Conclusion: Taken together, our findings show that: (1) women performed the Stroop test faster than men; (2) as older the person, lower is their cognitive performance; (3) the higher the education, the better the test execution time; (4) higher scores in the GFFI, leads to a better the performance in the Stroop test. Therefore, gender, age, education, functional fitness and muscle strength are predictors of cognitive performance in the elderly.
Objective: To unravel further evidences of age-related cognitive decline, we analyzed which physical and functional variables are predictors of cognitive performance in a sample of 498 Brazilian elderly (67.26% female). Methods: To do so, we used the Stroop test as a tool to evaluate executive functions and the general functional fitness index (GFFI) to evaluate the functional fitness of the participants. Results: A linear regression analysis reveals that gender, precisely female (β= - 0.097; t= - 2.286; p=0.023), age (β= 0.205; t= 4.606; p< 0.0001), education (β= - 0.280; t= - 6.358; p< 0.0001) and GFFI (β= - 0.101; t= - 2.347; p< 0.02) were predictor factors of cognitive performance. Body mass index (kg/m2) and nutritional status (underweight, eutrophic, overweight, or obese) were not predictors of cognitive performance of elderlies. Interestingly, among the GFFI tasks, it was noted a muscle power influence in the execution test time, both in upper and lower limbs (elbow flexion: β= - 0.201; t= - 4.672; p< 0.0001; sit-to-stand: β= - 0.125; t= - 2.580; p< 0.01). Conclusion: Taken together, our findings show that: (1) women performed the Stroop test faster than men; (2) as older the person, lower is their cognitive performance; (3) the higher the education, the better the test execution time; (4) higher scores in the GFFI, leads to a better the performance in the Stroop test. Therefore, gender, age, education, functional fitness and muscle strength are predictors of cognitive performance in the elderly.