Meingast Theodor, Carrier Bryson, Melvin Amanda, Kozloff Kenneth M, DeJong Lempke Alexandra F, Lepley Adam S
School of Kinesiology, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Kinesiology and Nutrition Sciences, University of Nevada-Las Vegas, Las Vegas, NV 89154, USA.
Sensors (Basel). 2025 Sep 5;25(17):5553. doi: 10.3390/s25175553.
Spatiotemporal running metrics such as cadence, stride length (SL), and ground contact time (GCT) are important for assessing performance and injury risk. However, such metrics are traditionally assessed using laboratory-based tools that are often inaccessible in applied settings. Wearable devices including smartwatches and lace-mounted inertial measurement units (IMUs) offer promising alternatives, yet cross-device agreement in reporting spatiotemporal variables remains unclear. This study evaluated agreement between a commercial smartwatch and lace-mounted IMUs across varied distances and environments in 65 physically active adults (33 female/32 male, height: 171.0 ± 8.9 cm; weight: 70.9 ± 15.2 kg). Participants completed indoor and outdoor runs (2.5 km, 5 km, 10 km, 20 km) wearing both devices simultaneously. Average cadence demonstrated acceptable agreement (MAPE = 4.1%, CCC = 0.66) and supported equivalence, particularly among males, during outdoor conditions, and longer run distances. In contrast, peak cadence showed weak correlation (MAPE = 5.3%, CCC = 0.29), and SL and GCT demonstrated poor agreement (MAPE = 14.9-19.0%, CCC = 0.30-0.39) across all conditions. While average cadence may serve as a metric for cross-device comparisons, especially for males, and longer-distance outdoor runs, other spatiotemporal metrics demonstrated poor agreement, limiting interchangeability. Understanding device-specific capabilities is essential when interpreting wearable-derived gait data. Further validation using gold-standard tools is needed to support accurate and applied use of wearable technologies.
诸如步频、步幅(SL)和地面接触时间(GCT)等时空跑步指标对于评估运动表现和受伤风险很重要。然而,传统上这些指标是使用基于实验室的工具来评估的,而这些工具在实际应用场景中往往难以获取。包括智能手表和鞋带式惯性测量单元(IMU)在内的可穿戴设备提供了有前景的替代方案,但在报告时空变量方面跨设备的一致性仍不明确。本研究评估了一款商用智能手表和鞋带式IMU在65名身体活跃的成年人(33名女性/32名男性,身高:171.0±8.9厘米;体重:70.9±15.2千克)不同距离和环境下的一致性。参与者同时佩戴这两种设备完成室内和室外跑步(2.5千米、5千米、10千米、20千米)。平均步频显示出可接受的一致性(平均绝对百分比误差 = 4.1%,组内相关系数 = 0.66),并支持等效性,特别是在男性中、户外条件下以及较长跑步距离时。相比之下,峰值步频显示出弱相关性(平均绝对百分比误差 = 5.3%,组内相关系数 = 0.29),并且步幅和地面接触时间在所有条件下都显示出较差的一致性(平均绝对百分比误差 = 14.9 - 19.0%,组内相关系数 = 0.30 - 0.39)。虽然平均步频可以作为跨设备比较的一个指标,特别是对于男性和较长距离的户外跑步,但其他时空指标显示出较差的一致性,限制了互换性。在解释可穿戴设备得出的步态数据时,了解特定设备的功能至关重要。需要使用金标准工具进行进一步验证,以支持可穿戴技术的准确和实际应用。