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Knee osteoarthritis (KOA) significantly impairs mobility in older adults. Understanding its impact on gait dynamics throughout the day is crucial for optimizing management strategies. This study aimed to explore diurnal variations in gait parameters among older adults with KOA using an in-shoe motion sensor (IMS) system equipped with accelerometers and gyroscopes. In this cross-sectional observational study, 19 older adults clinically diagnosed with early-stage KOA (Kellgren-Lawrence grades 1 or 2) participated. Key gait parameters were measured using an IMS system during morning (6:00 AM-11:59 AM) and afternoon (12:00 PM-5:00 PM) sessions. The IMS, placed bilaterally in the participants' shoes, continuously collected gait data during normal daily activities over a 24-hour period. Participants were instructed to walk for at least 10 min in each session. Data were analyzed using descriptive statistics, and paired t-tests or Wilcoxon signed-rank tests were applied to identify significant differences between sessions. Statistical significance was set at p < 0.05. The study included 19 participants (11 females, 8 males) with an average age of 71.4 ± 4.2 years. Walking speed decreased significantly from 1.06 ± 0.14 m/s in the morning to 0.99 ± 0.16 m/s in the afternoon (p = 0.028). Similarly, the maximum dorsiflexion angle decreased from 20.34° ± 2.98° to 18.80° ± 3.01° (p = 0.024), and the maximum plantar flexion angle decreased from 63.40° ± 5.84° to 60.79° ± 5.77° (p = 0.017) in the afternoon. Other parameters such as foot height, peak swing angular velocity, and maximum speed during the swing phase also showed significant reductions in the afternoon. Conversely, the roll angle of heel contact increased from 4.60° ± 2.62° to 5.53° ± 3.12° (p = 0.026), and stance time and pushing time increased significantly in the afternoon. Significant diurnal variations in gait parameters among older adults with KOA highlight the importance of considering time of day when evaluating gait and planning interventions. Wearable sensor technology enables continuous, objective gait monitoring in real-world settings, facilitating personalized and time-sensitive approaches for managing KOA.
Bandara et al. (Fri,) studied this question.