Knee osteoarthritis is a significant age-related condition that impairs mobility. Lab-based gait measurement has
identified specific gait mechanics that are associated with knee osteoarthritis severity and progression.
However, when interventions that successfully correct aberrant gait mechanics in the lab are implemented in
clinical trials, they do not meaningfully improve knee osteoarthritis symptoms or slow progression. This gap
between in-lab success and real-world efficacy may be because individuals walk differently in daily life than in
lab settings, with in-lab gait likely only being representative of a small portion of an individual’s real-world gait.
Longitudinal measurement of real-world gait could identify new factors that relate to joint health or intervention
effectiveness, enabling better prediction of knee osteoarthritis progression and improved intervention design.
Before we can design or execute a longitudinal study of real-world gait, we need a better understanding of real-
world gait data. Recent studies indicate that individuals walk slower and with shorter stride lengths on average
when out of the lab compared to in lab settings. These findings suggest that gait measures that are specifically
tied to joint health may also differ during daily life compared to the lab setting. Additionally, symptoms such as
pain and fatigue that are known to affect gait mechanics during in-lab collection vary substantially within and
between days in real-world settings (and likely to a greater extent than in lab settings). Despite advances in
wearable sensors such as inertial measurement units (IMUs) and the popularity of using IMUs for gait analysis
in the lab, the use of IMUs in real-world gait analysis has been limited because of challenges with analyzing
unobserved data and interpreting outcomes from novel data. Our team has successfully implemented reliable
methods for detecting and categorizing walking activity (e.g., level walking vs. stairs, straight walking vs. turns),
orienting data to recognizable reference frames (e.g., gravitational or functional), and calculating interpretable
outcomes that correspond to traditional gait measures and are relevant to joint function (e.g., knee joint range
of motion, propulsive ankle angular velocity, inter-segment coordination). In this study, we will calculate our
established gait measures during real-world gait collected over 3 full, consecutive days in 3 groups of
participants: older adults with knee osteoarthritis, older asymptomatic adults, and young adults. We will use
electronic messaging to collect ecologically valid measures of pain and fatigue 5 times each day. We will use
these data to compare the magnitude and variance of knee range of motion, propulsive ankle joint velocity, and
lower extremity coordination between groups, days, and between real-world and lab settings (Aim 1). We will
model the relationships between our gait measures and participant self-reported pain and fatigue (Aim 2).
Completion of these aims will provide preliminary data with which we can design a larger study (R01) to
evaluate the role of real-world gait in knee osteoarthritis progression. In the long term, this knowledge will allow
for earlier detection of mobility decline and improved intervention design and implementation.