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High-Intensity Interval Circuit Training Versus Moderate-Intensity Continuous Training on Functional Ability and Body Mass Index in Middle-Aged and Older Women: A Randomized Controlled Trial.

Ismael Ballesta-García | Ignacio Martínez-González-Moro | Jacobo Á Rubio-Arias | María Carrasco-Poyatos
International journal of environmental research and public health | 2019

The literature suggests that high-intensity interval training (HIIT) is more effective than moderate-intensity continuous training (MICT) to improve functional ability. However, there is no evidence on including HIIT in a circuit programme (HIICT). Our objective was to determine what type of training (HIICT or MICT) induces greater adaptations in the functional ability and body mass index of middle-aged and older women. The study used a quasi-experimental randomized controlled trial with 54 participants (age = 67.8 ± 6.2 years). Participants were randomly allocated to HIICT (n = 18), MICT (n = 18) or a non-exercise control group (CG; n = 18). The participants in the HIICT or MICT groups trained twice a week (1 h/session) for 18 weeks. Forty-one subjects were analysed (HIICT; n = 17, MICT; n = 12, CG; n = 12). Five subjects presented adverse events during the study. Strength, gait, cardiorespiratory fitness, balance and body mass index were measured. A significant training x group interaction was found in the arm curl test, where HIICT was statistically better than MICT and CG. Likewise, HIICT was statistically better than the CG in the BMI interaction. In lower limb strength, gait/dynamic balance and cardiorespiratory fitness, both HIICT and MICT were statistically better than the CG. In conclusion, HIICT generated better adaptations in upper limb strength than MICT. Likewise, HIICT generated better adaptations in body mass index than CG. Finally, both HIICT and MICT had a similar influence on strength, cardiorespiratory fitness and gait/dynamic balance.

Pubmed ID: 31671584

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Software for statistical analysis and spreadsheet editing that is built on top of the R statistical language. It encourages a “community driven” philosophy, where users can develop and publish their analyses to make them available to a wide audience.

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