Aging significantly impacts mobility and balance, posing challenges for public health systems, particularly in countries like Colombia with rapidly growing elderly populations. Traditional physiotherapy programs often face adherence issues due to low participant motivation. This study evaluates the effectiveness of Moto Tiles, an interactive cognitive-motor training tool, in improving functional mobility among older adults through kinematic analysis of the instrumented Timed Up-and-Go test (iTUG). A quasi-experimental pre-post design compared an intervention group (n = 21) undergoing a 10-week Moto Tiles program with a control group (n = 7) receiving non-physical cognitive stimulation. Inertial sensors (Xsens IMUs) captured kinematic data during iTUG, focusing on variables such as gait speed, cadence, step count, and stride parameters. Linear Mixed Models (LMMs) with Holm-Bonferroni correction were employed to account for repeated measures and multiple comparisons. The study underscores the value of instrumented assessments like iTUG for granular mobility analysis and supports integrating interactive technologies into geriatric physiotherapy to optimize functional outcomes.

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Kinematic Data Analysis of the iTUG: Quantitative Assessment of Moto Tiles Effectiveness in Older Adults

  • Johanna Vargas-Adames,
  • Javier Almeida-Moreno

摘要

Aging significantly impacts mobility and balance, posing challenges for public health systems, particularly in countries like Colombia with rapidly growing elderly populations. Traditional physiotherapy programs often face adherence issues due to low participant motivation. This study evaluates the effectiveness of Moto Tiles, an interactive cognitive-motor training tool, in improving functional mobility among older adults through kinematic analysis of the instrumented Timed Up-and-Go test (iTUG). A quasi-experimental pre-post design compared an intervention group (n = 21) undergoing a 10-week Moto Tiles program with a control group (n = 7) receiving non-physical cognitive stimulation. Inertial sensors (Xsens IMUs) captured kinematic data during iTUG, focusing on variables such as gait speed, cadence, step count, and stride parameters. Linear Mixed Models (LMMs) with Holm-Bonferroni correction were employed to account for repeated measures and multiple comparisons. The study underscores the value of instrumented assessments like iTUG for granular mobility analysis and supports integrating interactive technologies into geriatric physiotherapy to optimize functional outcomes.