Effects of two different dual-task training protocols on gait, balance, and cognitive function in community-dwelling older adults: a 24-week randomized controlled trial.

Background: Although alternating dual-task (ADT) training is functionally easier for older adults, a large part of the motor and cognitive tasks is simultaneously performed, especially during activities of daily living that require maintaining body balance. Objective: To evaluate the effects of mixed dual-task training on mobility, cognitive function, and balance in community-dwelling older adults. Methods: Sixty participants were randomly allocated at a 1:1 ratio into the experimental group-single motor task (SMT) and simultaneous dual task (SDT) interchangeably in stage 1 (for 12 weeks) and after strictly with SDT in stage 2 (the last 12 weeks)-or into the control group-only SMT and SDT interchangeably in stages 1 and 2. Gait parameters were acquired by two inertial sensors. Physical and cognitive performance were acquired by specific questionnaires. Generalized linear mixed models were used for analyzing interaction and main effects. Results: No between-group difference was observed for gait performance. Both protocols improved mobility (mean change ((MC) = 0.74)), dual-task effect (MC = -13.50), lower limb function (MC = 4.44), static (MC = -0.61), and dynamic balance (MC = -0.23), body sway (MC = 4.80), and cognitive function (MC = 41.69). Conclusion: Both dual-task training protocols improved these outcomes.

Concern About Falling, Confidence in Balance, Quality of Life, and Depression Symptoms in Community-Dwelling Older Adults After a 24-week Dual-Task Training With Variable and Fixed Priority: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the effect of a 24-week dual-task training with progression from variable to fixed priority on the concern about falling, confidence in balance, quality of life, and depression symptoms in community-dwelling older adults. METHODS: A total of 60 participants (60-80 y.o.) were randomly allocated into a dual-task training group with progression from variable to fixed priority (experimental group) or into a dual-task training group with variable priority (control group). RESULTS: No between-group difference was observed after the intervention. A significant time effect showed a reduction in concern about falling [mean difference (MD) = -2.91)] and depression symptoms (MD = -1.66), an increase in the physical function (MD = 7.86), overall mental health (MD = 5.82), perception of vitality, energy, and less fatigue (MD = 10.45), general perception of overall health (MD = 6.81), and their health compared to the last year (MD = 11.89). CONCLUSION: The experimental protocol was not superior to the control one. However, both protocols improved these outcomes.

Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach.

BACKGROUND: Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. METHODS: Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). RESULTS: The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. DISCUSSION: The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent.