Background: Quantitative gait analysis is essential in both clinical and research contexts; however, traditional marker-based motion capture systems are costly and burdensome. Advances in three-dimensional markerless motion capture (3D-MMC) offer more accessible alternatives; however, they lack standardized protocols. Objectives: The present study aimed to establish a standardized protocol and procedures for 3D MMC-based gait analysis using OpenCap and to quantify the reliability and within-session precision of key spatiotemporal gait parameters. Methods: Fifty healthy university students (mean age = 22.15 ± 2.12 years) completed walking trials along a 10 m walkway under single-task (ST) and five dual-task (DT) conditions of varying cognitive complexity. Gait data were collected using a two-camera OpenCap 3D-MMC system, with standardized calibration, lighting, clothing, and trial segmentation. Spatiotemporal parameters were extracted, and within-session relative reliability was quantified using two-way mixed-effects intraclass correlation coefficients, and absolute reliability was quantified using general linear model–derived within-subject error (standard error of measurement, SEM) and minimal detectable change (MDC). Repeated-measures ANOVA with Bonferroni corrections were used to examine condition-related differences. Results: Of 500 trials, 491 (98.2%) were successfully processed. Within-subject test–retest reliability ranged from moderate to excellent for all variables, with gait speed, stride length, and cadence showing the highest ICCs and smallest SEM and MDC values, and step width and double support exhibiting larger measurement error. Conclusions: This study establishes a standardized 3D-MMC protocol for gait analysis using OpenCap and demonstrates good to excellent within-session relative and absolute reliability for most spatiotemporal gait parameters in healthy young adults. Dual-task walking is used here to illustrate how trial-averaged OpenCap measurements and their SEM/MDC can be used to determine which condition-related changes in gait exceed measurement error.

Objective: This study aims to explore the relationship between objectively measured screen time and depression. Moreover, it seeks to determine whether social support mediates the relationship between depression and objectively measured screen time. Methods: This cross-sectional study included a sample of 121 international college students. Screen time was measured using logged data from participants’ phones. Basic anthropometric data were collected with a stadiometer and bioimpedance analysis. The Zung Depression Scale was used to assess depression, and the Interpersonal Support Evaluation List was applied to assess social support. Data analysis involved correlation and t-test analyses, followed by a path mediation analysis. Results: The current data suggest that there is no direct association between depression and screen time, as no correlation was found between these variables (r(85) = .03, p > .05), nor were meaningful differences in screen time observed between depressed and non-depressed groups (t(85) = –.19, p = 0.85). However, an indirect association was established through a path model in which social support acted as a mediator (p < .05). Conclusion: Screen time alone is not indicative of depression; however, social support can significantly mediate their relationship. Consequently, lowering screen time may not lead to a reduction in depressive symptoms, as more subtle factors may influence this association. Therefore, the relationship between phone use and depression is complex and requires further investigation using more advanced methods.