■科米尔,P,梅兰,C,琼脂-纽曼,D,Geneau,D,Epp-Stobbe,A,Lenetsky,S,还有Klimstra,M.用于线性冲刺剖面的可穿戴卫星系统技术的系统回顾和荟萃分析:技术创新和实际应用。JStrengthCondRes38(2):405-418,2024-一种新兴且有前途的做法是使用全球导航卫星系统(GNSS)技术在训练和比赛中描绘团队运动运动员。因此,采用荟萃分析进行叙述性系统综述的目的是评估有关在sprint建模中使用卫星系统传感器的文献,并巩固研究结果以评估其有效性和可靠性.遵循系统审查和荟萃分析(PRISMA)指南的首选报告项目,对数据库的电子搜索,PubMedandSPORTDiscus(EBSCO),进行了。同时考虑了效度和信度研究,从最初确定的1,485项研究中保留了16项研究用于审查.对结果的影响以标准化平均差(SMD,科恩d)对于每个结果(即,最大冲刺速度[MSS],加速常数[τ],最大理论速度[V0],相对力[F0],和相对功率[Pmax])。影响幅度表示GNSS推导和准则推导之间的SMD(即,雷达和激光),并得出以下估计:MSS较小(d=0.22,95%CI0.01至0.42),τ(d=-0.18,95%CI-0.60至0.23),V0(d=0.14,95%CI-0.08至0.36),相对F0(d=0.15,95%CI-0.25至0.55),和相对Pmax(d=0.21,95%CI-0.16至0.58)。在荟萃分析研究中没有发现发表偏倚,主持人分析显示有几个因素(采样率和传感器制造商)影响了结果。研究之间的异质性被认为是中等到高。这突出了传感器技术差异研究之间的差异(即,采样率,传感器融合,和卫星网络获取),加工技术,使用的标准技术,sprint协议,结果报告,运动员的特点。这些发现可能有助于指导使用GNSS技术进行冲刺建模的改进,并在未来的研究中实现更直接的比较。当执行稳健且一致的数据处理协议时,可以将使用GNSS技术实施全面线性冲刺的工作集成到特定于运动的会话中,以进行冲刺建模。这对疲劳监测具有重要意义,程序设计,系统测试,以及个人和团队运动中的康复。
UNASSIGNED: Cormier, P, Meylan, C, Agar-Newman, D, Geneau, D, Epp-Stobbe, A, Lenetsky, S, and Klimstra, M. A systematic
review and meta-analysis of wearable satellite system technology for linear sprint profiling: technological innovations and practical applications. J Strength Cond Res 38(2): 405-418, 2024-An emerging and promising practice is the use of global navigation satellite system (GNSS) technology to profile team-sports athletes in training and competition. Therefore, the purpose of this narrative systematic
review with meta-analysis was to evaluate the literature regarding satellite system sensor usage for sprint modeling and to consolidate the findings to evaluate its validity and reliability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an electronic search of the databases, PubMed and SPORTDiscus (EBSCO), was conducted. Concurrent validity and reliability studies were considered, and 16 studies were retained for the
review from the initial 1,485 studies identified. The effects on outcomes were expressed as standardized mean differences (SMDs, Cohen\'s d ) for each outcome (i.e., maximal sprint speed [MSS], the
acceleration constant [τ], maximal theoretical velocity [ V0 ], relative force [ F0 ], and relative power [P max ]). Effect magnitudes represented the SMD between GNSS-derived and criterion-derived (i.e., radar and laser) and resulted in the following estimates: small for MSS ( d = 0.22, 95% CI 0.01 to 0.42), τ ( d = -0.18, 95% CI -0.60 to 0.23), V0 ( d = 0.14, 95% CI -0.08 to 0.36), relative F0 ( d = 0.15, 95% CI -0.25 to 0.55), and relative P max ( d = 0.21, 95% CI -0.16 to 0.58). No publication bias was identified in meta-analyzed studies and moderator analysis revealed that several factors (sampling rate and sensor manufacturer) influenced the results. Heterogeneity between studies was considered moderate to high. This highlighted the differences between studies in sensor technology differences (i.e., sampling rate, sensor fusion, and satellite network acquisition), processing techniques, criterion technology used, sprint protocols, outcome reporting, and athlete characteristics. These findings may be useful in guiding improvements in sprint modeling using GNSS technology and enable more direct comparisons in future research. Implementation of all-out linear sprint efforts with GNSS technology can be integrated into sport-specific sessions for sprint modeling when robust and consistent data processing protocols are performed, which has important implications for fatigue monitoring, program design, systematic testing, and rehabilitation in individual and team sports.