sEMG wavelet-based indices predicts muscle power loss during dynamic contractions.

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F(med)), Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI(nsm5) and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.

[Models to explain and predict medical case-loads: their use in calculating the maximum family medicine list that allows at least ten minutes per consultation]. / Modelos explicativos y predictivos de la carga asistencial médica: aplicación para el cálculo del cupo máximo en medicina de familia que permita un mínimo de diez minutos por consulta.

OBJECTIVE: To calculate the maximum family medicine list that gives at least ten minutes per consultation.Design. Transversal. SETTING: Three health centres. SUBJECTS: 45 826 inhabitants. MEASUREMENTS: We used the appointments made at three centres to calculate the annual time employed per patient and we adjusted it to allocate a minimum of ten minutes per consultation. We established a cubic regression model to predict the mean case-load per age of patient in general medicine and calculated the maximum list if 70% of the working day were dedicated to care. The results contrasted two centres with greater nursing involvement and one with less. We showed the R2 coefficients. We calculated the maximum lists for the health centres of Navarra and showed them in five clusters worked out on the basis of the percentage of patients >=65. RESULTS: Age explained 86.1% of variability in mean case-load at each age (84% in children and 93.5% in adults). According to the mean percentage of those >=65 years old, the average maximum lists for centres with more or less nursing involvement are as follows: 7.0%>=65 (2025 and 1989); 14.0% (1834 and 1715); 21.2% (1691 and 1558); 27.0% (1648 and 1460), 34.0% (1560 and 1340). CONCLUSION: To a great extent, age explains the variability in case-load and lets us calculate the maximum number of patients on the list that still ensures a minimum time for each consultation.

Modelos explicativos y predictivos de la carga asistencial médica: aplicación para el cálculo del cupo máximo en medicina de familia que permita un mínimo de diez minutos por consulta / Models to explain and predict medical case-loads: their use in calculating the maximum family medicine list that allows at least ten minutes per consultation

Objetivo. Estimar el cupo máximo en medicina familiar que permita consultas mínimas de 10 min. Diseño. Transversal. Ámbito. Tres centros de salud. Sujetos. Un total de 45.826 habitantes. Mediciones. A partir de las citas de tres centros estimamos el tiempo anual empleado por paciente y lo ajustamos asignando un tiempo mínimo de 10 min por consulta. Establecemos un modelo de regresión cúbica predictor de la carga de trabajo asistencial media por edad en medicina general y estimamos el cupo máximo si se dedica un 70 por ciento de la jornada laboral a la asistencia. Exponemos los resultados para dos centros con mayor implicación de enfermería en la atención de los pacientes, y otro en que ésta es menor. Presentamos los coeficientes R2. Estimamos los cupos máximos para los centros de salud de Navarra y los mostramos en cinco grupos elaborados a partir del porcentaje de población de 65 años o más. Resultados. La edad explica el 86,1 por ciento de la variabilidad en la carga asistencial media en cada edad (un 84 por ciento en niños y un 93,5 por ciento en adultos). Según el porcentaje promedio de sujetos 65 años de edad los cupos máximos promedios para centros con mayor o menor implicación de enfermería son los siguientes: 7 por ciento (2.025 y 1.989); 14 por ciento (1.834 y 1.715); 21,2 por ciento (1.691 y 1.558); 27 por ciento (1.648 y 1.460); 34 por ciento (1.560 y 1.340). Conclusiones. La edad explica en gran medida la variabilidad de la carga asistencial y permite estimar el cupo máximo de pacientes que asegure un tiempo mínimo por consulta (AU)