Our Research

Our findings support a move away from treating fall risk as a single score or a single impairment.

An initial hierarchical cluster analysis examined how 66 measures from gait, cognition, physiological performance, and fear of falling changed together. Four groupings emerged: cognitive factors; fast-gait characteristics; physiological performance and fear of falling; and step width or base-of-support measures across walking speeds.

A subsequent machine-learning analysis shifted the focus from clustering measures to clustering people. Five clinically interpretable profiles were identified: Slow Walkers, Fearful but Fit, Strategy Shifters, Visually Dependent Balancers, and Resilient Agers. These profiles aligned with established clinical classifications while revealing important differences among people assigned to similar overall risk levels.

Together, this work suggests that data science can complement clinical judgment by helping clinicians recognize distinct patterns of need and select more targeted assessment and prevention strategies.

Safe mobility depends on how people process information and how confident they feel, not only on physical capacity.

These findings support assessing cognition and confidence alongside balance, strength, sensation, and gait. They also caution against assuming that fear of falling is simply a negative factor, because confidence must be interpreted in relation to actual capacity.

Walking is a central indicator of mobility, but the way gait is measured changes what clinicians learn.

Analysis of 340 older adults showed that the first walking trial is best treated as acclimation. For preferred-speed gait, averaging trials two through four produced the most consistent estimate, while a fifth trial may introduce fatigue or motivational changes. This creates a practical four-trial clinical protocol.

Higher physiological fall-risk scores were associated with slower height-normalized gait velocity, lower cadence, shorter steps, more time in stance, and more reported falls. A pilot analysis also found that anteroposterior margin of stability was related to the PPA and to balance, proprioception, vision, cognition, and gait measures.

Emerging work, including RDE-GAIT, is expanding the program toward three-dimensional gait symmetry and new methods for connecting biomechanical measures with clinical interpretation.

The chewing and walking studies provide the motor-control foundation for the current healthy-aging and fall-risk program.

Chewing and walking are both rhythmic behaviors. Our work found that preferred chewing rates were preserved in older adults even though older adults walked more slowly. When chewing rate was deliberately changed, participants adjusted stepping rate and gait velocity, demonstrating coupling between rhythmic motor systems.

Additional studies showed that the effect of performing two movements at once depends on the type of task. Rhythmic actions can become synchronized, while discrete actions such as reaction-time responses may slow because the tasks compete for processing resources.

Across childhood, young adulthood, and older adulthood, reaction time and postural sway showed different age-related patterns than gait or chewing. This reinforces a central principle of the current program: movement capacity is task-specific, multidimensional, and influenced by the demands placed on the individual.

This line of research connects directly to current work on cognitive-motor interaction, movement variability, gait adaptation, measurement consistency, and the ability to respond to changing environmental demands.

Legacy and Continuity

Monarch Balance for Life builds on the research program established by founding director Steven Morrison. These areas should remain on the website as foundational work, written in past tense and clearly connected to the initiative’s current direction.