Force plates represent the "gold standard" in measuring running kinetics to predict performance or to identify the sources of running-related injuries. As these measurements are generally limited to laboratory analyses, wireless high-quality sensors for measuring in the field are needed. This work analysed the accuracy and precision of a new wireless insole forcesensor for quantifying running-related kinetic parameters. Vertical ground reaction force (GRF) was simultaneously measured with pit-mounted force plates (1 kHz) and loadsol sensors (100 Hz) under unshod forefoot and rearfoot running-step conditions. GRF data collections were repeated four times, each separated by 30 min treadmill running, to test influence of extended use. A repeated-measures ANOVA was used to identify differences between measurement devices. Additionally, mean bias and Bland-Altman limits of agreement (LoA) were calculated. We found a significant difference (p < .05) in ground contact time, peak force, and force rate, while there was no difference in parameters impulse, time to peak, and negative force rate. There was no influence of time point of measurement. The mean bias of ground contact time, impulse, peak force, and time to peak ranged between 0.6% and 3.4%, demonstrating high accuracy of loadsol devices for these parameters. For these same parameters, the LoA analysis showed that 95% of all measurement differences between insole and force plate measurements were less than 12%, demonstrating high precision of the sensors. However, highly dynamic behaviour of GRF, such as force rate, is not yet sufficiently resolved by the insole devices, which is likely explained by the low sampling rate.
The TAP1 and TAP2 genes, located in the HLA class II region, encode subunits of a peptide transporter. Both genes display limited genetic variability; four different nucleotide substitutions have been found in the TAP2 gene. Here studies on linkage disequilibrium between TAP2 variants and HLA class II alleles are reported, in an attempt to evaluate whether TAP2 variants are associated with insulin-dependent diabetes mellitus (IDDM). As reported previously, a significant decrease of homozygosity for TAP2 alleles encoding alanine at residue 665 (665 Ala) and glutamine at 687 (687 Gln) paralleled by an increase in homozygosity for TAP2 alleles encoding threonine at residue 665 (665 Thr) and a stop codon at 687 (687 Stop), was found in both Finnish and Norwegian IDDM patients compared to random controls. However, a strong linkage disequilibrium between these TAP2 polymorphisms and given HLA-DR and -DQ genes was observed among healthy controls. The frequent 665 Thr and 687 Stop variants were in linkage disequilibrium both with the DR4-DQ8 and the DR3-DQ2 haplotypes, haplotypes which are strongly associated with IDDM. In contrast, the DR1-DQ5 and DR13-DQ6 (e.g. DQB1*0603) haplotypes, which are decreased among IDDM patients, were associated with the 665 Ala and 687 Gln variants. Thus, when DR- and DQ-matched patients and controls were compared, associations of the investigated TAP2 variants and IDDM were no longer detectable. These data, therefore, indicate that the associations previously found between certain TAP2 variants and IDDM are secondary to a primary association between this disease and particular DQ alpha beta heterodimers.
Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotemies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r2) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r2 (p<0.001) and caused αo,knee to converge toward 11.9±8.0 degrees of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5 to 0.7±0.2 degrees (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction-adduction minimization algorithm (Baker et al., 1999) and better than a null space algorithm (Schwartz and Rozumalski, 2005) for this healthy subject population.
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