Matthew Smuck

Background: Wearable inertial measurement units (IMUs) offer scalable, objective gait assessment, but detailed stride-level validation against motion capture and their ability to reflect physical activity (PA)–related gait differences in knee osteoarthritis (KOA) remain incompletely characterized. Objective: This study aimed to evaluate the technical validity of foot-mounted IMU–derived gait parameters compared with optical motion capture in individuals with KOA and healthy controls. As a secondary exploratory aim, we assessed whether IMU-derived gait parameters differed across self-reported PA levels. Methods: In this pilot cross-sectional validation study, 20 participants (KOA: n=10, 50%; healthy controls: n=10, 50%) completed 3 standardized walking conditions (self-paced, fast-paced, and endurance-equivalent). IMU-derived gait parameters were compared with motion capture at the stride level (≥5000 strides) using Pearson correlation, intraclass correlation coefficients (ICCs), mean absolute error, root mean square error, and Bland-Altman analysis. Participants completed the Stanford Brief Activity Survey to categorize PA level. Secondary analyses emphasized effect sizes and 95% CIs given the pilot design. Feasibility was assessed by protocol completion, data completeness, and tolerability. Results: IMU-derived gait speed demonstrated strong agreement with motion capture ( r =0.99; ICC=0.98, 95% CI 0.95‐0.99; mean absolute error=0.07 m/s; bias=0.01 m/s; 95% limits of agreement −0.14 to 0.16 m/s). Stride length and cadence showed similarly high agreement (ICC range 0.96‐0.97). All participants completed the protocol with minimal adverse events. Exploratory analyses suggested that participants reporting higher PA demonstrated directionally faster gait speed and longer stride length, with small-to-moderate effect sizes, whereas inactive participants exhibited higher stance-related rhythm parameters. Conclusions: Foot-mounted IMUs demonstrated strong stride-level agreement with motion capture across walking conditions, supporting technical validity in both KOA and healthy populations. Exploratory findings suggest that wearable-derived gait parameters may reflect activity-related differences; however, larger longitudinal studies are required to confirm these preliminary signals.

Background: Research on the demographics of collegiate sport participation lacks depth. The role of gender remains understudied despite keen public interest in the intersection of gender and sport. Methods: To examine trends and associations between gender and collegiate sport participation we analyzed data from the 2015-2024 National College Health Assessment survey derived from >1000 colleges across the United States. Gender was self-reported. Sport participation was assessed through yes/no responses to the questions: “Do you participate in intramural/club/varsity sports?” Statistical analyses were calculated overall and by gender categories. We hypothesized that queer gender is negatively associated with sport participation and will demonstrate declining participation over time relative to cisgender students. Findings: We studied 939,554 student respondents, average age 22·8 years, with the following gender breakdown: 30·2% cisgender men, 65·6% cisgender women, 0·6% transgender men, 0·7% transgender women, and 2·9% nonbinary. Within this population the sport participation rate was 21·7% at one or more levels (5·7% varsity, 8·2% club, and 12·8% intramural). Transgender students participated at a rate 8·48 percentage points lower than cisgender (95% CI -9·00 to -7·94, p<0.01) while women participated 11·7 percentage points less than men (95% CI -12·0 to -11·4, p<0.01). Moreover, there was a significant and steady decline in participation (-1·05 percentage points/year, 95% CI -1·08 to -1·02, p<0.01) across all gender categories that was more pronounced among transgender students compared to their cisgender peers (-0·247 percentage points/year steeper, 95% CI -0·43 to -0·07 p<001). Interpretation: This study found that gender has a significant and distinct impact on organized collegiate sport participation. It also reveals a statistically significant and persistent decade-long decline in collegiate sport participation impacting all genders. These results demonstrate a need to explore factors related to gender-based differences and declines in sport participation.

This study investigates the link between changes in physical activity (PA) measured by wearable accelerometers and the worsening of knee osteoarthritis (KOA) symptoms over two years. Using data from 782 participants in the Osteoarthritis Initiative accelerometer sub-study, PA was tracked with hip-worn ActiGraphs. Participants were classified as “worsening” if their Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score increased by >10 points and as “stable” otherwise. PA was categorized into daily counts and minutes spent in various intensity levels, and analyzed in 3 h intervals across the day. Of the participants, 123 (15.7%) experienced worsening symptoms. At baseline, both groups had similar characteristics aside from slower sit-to-stand times in the worsening group. Over two years, the worsening group had a greater decline in total daily activity counts (−18% vs. −10%) and more significant reductions during late afternoon and evening (15:00–21:00; −21% vs. −6%). This group also showed a notable decrease in gait speed, longer sit-to-stand times, and a trend towards greater medial joint space narrowing. These findings suggest that larger declines in PA, especially in activities in the late afternoon and evening, are associated with worsening KOA symptoms, although causality cannot be established.

Accurate measurement of physical activity (PA) using accelerometers is limited by sensitivities to sensor placement, particularly between the wrist and hip. This study evaluates these differences and proposes a harmonization approach to improve comparability among individuals with chronic low back pain. Data from the comeBACK study (NIH BACPAC, 1U19AR076737-01) included 179 participants (mean age 54.6 years, 56.4% female) who wore ActiGraph GT3X+ accelerometers on both the nondominant wrist and right hip for 7 days. PA metrics—step count, sedentary time, light physical activity, and moderate to vigorous physical activity—were analyzed. On average, wrist step counts were 3,500 steps higher per day than hip counts, with greater bias at lower activity levels. A piecewise linear regression model was developed to translate wrist-derived step counts to hip-equivalent values. Wrist cutpoints derived from percentile-matched resulted in less than 5-min bias in sedentary estimate. Moderate to vigorous physical activity estimates from wrist and hip were not directly comparable; although percentile-matching cutpoint reduced bias, it resulted in high variance. Light physical activity showed moderate agreement between wear locations but also exhibited high variance. Our findings reveal significant disparities in PA estimates from wrist- and hip-worn sensors, emphasizing the importance of cautious data interpretation across different wear locations. We quantified sensor bias and proposed calibration methods to support accurate comparisons of data from these two wear locations. These findings are valuable for researchers and clinicians aiming to assess PA in individuals with low back pain and harmonize data from diverse sensor placements.