Ageing, Muscle Power and Physical Function: A Systematic Review and Implications for Pragmatic Training Interventions

Abstract

Maximal intended movement velocity, low training load, simple training methods, low-volume training and low-frequency training were revealed as components offering potential for the development of a pragmatic intervention. Additionally, the research area is dominated by short-term interventions producing short-term gains with little consideration of the long-term maintenance of functional performance. We believe the area would benefit from larger and higher-quality studies and consideration of optimal long-term strategies to develop and maintain muscle power and physical function over years rather than weeks.

Effects of a deep-water running program on muscle function and functionality in elderly women community dwelling

“…Strength of the Knee extensor muscle was used due to relevance of the lower limb strength to perform daily living activities such as ascending and descending stairs, rising from a chair 23 and to avoid falls 24 . Power analysis was calculated using OpenEpi v. 3.…”

“…This suggests that the tests were able to capture the muscle function improvement obtained through water exercise program. The estimated relative effort during walking at the hip and knee extensors muscles are 27% and 30% of the maximal strength respectively, thus maximal strength increases may result in less absolute effort to walk 23 . In addition, gait velocity has been considered as an indicator of vitality and health 32 .…”

Influência dos treinamentos funcional e tradicional na potência muscular, qualidade de movimento e qualidade de vida em idosas: um ensaio clínico randomizado e controlado

“…Byrne 22 reported that a good intervention with physical training should develop strength and muscle power for better performance in the ADLs. In this variable, both types of training presented significant increases in relation to pretest, since they emphasize the maximum concentric velocity in the performance of movements.…”

Prediction of cardiorespiratory fitness from self-reported data in elderly

“…The model included the variables sex, age, BMI, self-reported physical activity and resting heart rate (R 2 adjusted=0.51). Maranhão Neto et al 22 developed a model in a sample of 60-91 year, including the variables age, self-reported fitness and relative handgrip strength (R 2 adjusted=0.79). Variables such as resting heart rate and handgrip strength demand appropriate equipment and preparation, which limit the applicability of the models in population-based studies.…”