Design and Validation of a Novel Method to Measure Cross-Sectional Area of Neck Muscles Included during Routine MR Brain Volume Imaging

Low muscle mass secondary to disease and ageing is an important cause of excess mortality and morbidity. Many studies include a MR brain scan but no peripheral measure of muscle mass. We developed a technique to measure posterior neck muscle cross-sectional area (CSA) on volumetric MR brain scans enabling brain and muscle size to be measured simultaneously.We performed four studies to develop and test: feasibility, inter-rater reliability, repeatability and external validity. We used T1-weighted MR brain imaging from young and older subjects, obtained on different scanners, and collected mid-thigh MR data.After developing the technique and demonstrating feasibility, we tested it for inter-rater reliability in 40 subjects. Intraclass correlation coefficients (ICC) between raters were 0.99 (95% confidence intervals (CI) 0.98-1.00) for the combined group (trapezius, splenius and semispinalis), 0.92 (CI 0.85-0.96) for obliquus and 0.92 (CI 0.85-0.96) for sternocleidomastoid. The first unrotated principal component explained 72.2% of total neck muscle CSA variance and correlated positively with both right (r = 0.52, p = .001) and left (r = 0.50, p = .002) grip strength. The 14 subjects in the repeatability study had had two MR brain scans on three different scanners. The ICC for between scanner variation for total neck muscle CSA was high at 0.94 (CI 0.86-0.98). The ICCs for within scanner variations were also high, with values of 0.95 (CI 0.86-0.98), 0.97 (CI 0.92-0.99) and 0.96 (CI 0.86-0.99) for the three scanners. The external validity study found a correlation coefficient for total thigh CSA and total neck CSA of 0.88.We present a feasible, valid and reliable method for measuring neck muscle CSA on T1-weighted MR brain scans. Larger studies are needed to validate and apply our technique with subjects differing in age, ethnicity and geographical location

Evidence-based practice: Tools and techniques.

Evidence-based practice (EBP) requires the conscious, conscientious and explicit application of the best available research evidence, together with professional expertise and patient/customer choice, to work practices. From its origins in clinical medicine, through a broader application to the health services industry, an evidence-based approach to work practices is becoming increasingly influential in all human services. Implementing evidence-based practice is related to the organisational management concepts of Continuous Quality Improvement (CQI), Knowledge Management and the Learning Organisation. For human services professionals finding, critically appraising and applying best evidence requires enhanced information and knowledge management skills. Central to these skills is an understanding of the information ecology, particularly for the multi-disciplinary AOD field. This paper introduces the data-information-knowledge continuum, levels of evidence and the tools and techniques of finding and critically appraising evidence. Examples relevant to the AOD field are provided

Vascular adaptations in nonstimulated areas during hybrid cycling or handcycling in people with a spinal cord injury: a pilot study of 10 cases.

STUDY DESIGN: Sub-study of a randomized controlled trial. OBJECTIVES: To examine if hybrid cycling (cycling with the legs via electrical stimulation combined with voluntary handcycling) compared to handcycling leads to different systemic vascular adaptations in individuals with a long-term spinal cord injury (SCI). SETTING: Two rehabilitation centers in the Netherlands. METHODS: Ten individuals with a SCI trained on a hybrid bicycle (N = 5) or a handcycle (N = 5) for 16 weeks twice a week. Prior to and following the training the intima media thickness (IMT) of the common coronary artery (CCA) and superficial femoral artery (SFA) were measured and the flow-mediated dilation (FMD) of the brachial artery (BA) was analyzed. RESULTS: Before training, there were no significant differences in any of the outcome measures between the groups. We found no change in CCA IMT (pre: 0.616 mm, post: 0.586 mm), or in SFA (pre: 0.512 mm, post: 0.520 mm) after hybrid cycling. We also found no change in FMD % of BA after hybrid cycling (pre: 9.040%, post: 9.220%). There were no changes in CCA IMT, SFA IMT, and FMD% after handcycling either. CONCLUSIONS: It appears that 16 weeks of twice-weekly training of up to 30 min on a hybrid bicycle or handcycle does not lead to systemic vascular adaptations. A larger sample size and training protocol with more frequent and higher intensity training (which might involve a home-based setting and an adapted period prior to the training) might show different results

Relationship between physical strain during standardised ADL tasks and physical capacity in men with spinal cord injuries

To describe physical strain during activities of daily living (ADL), 44 men with spinal cord injuries (C4-L5) performed a set of standardised tasks. The physical strain was defined as the highest heart rate response expressed as a percentage of the individual heart rate reserve (%HRR). The physical strain averaged over the subjects who performed all tasks (n = 24) was (mean +/- SD): 20.2 +/- 7.2 %HRR (washing hands), 20.4 +/- 7.3 %HRR (passing a side-hung door), 28.8 +/- 10.8 %HRR (transfer to a toilet), 31.2 +/- 13.1 %HRR (ascending an 8 cm curb). 33.9 +/- 12.0 %HRR (transfer to a shower seat), 35.1 +/- 10.5 %HRR (transfer to bed), 36.4 +/- 13.3 %HRR (preparing lunch), 37.1 +/- 12.0 %HRR (washing up), 38.7 +/- 14.9 %HRR (ascending a ramp), 39.8 +/- 15.6 %HRR (transfer to a shower wheelchair), 41.4 +/- 12.1 %HRR (changing sheets), and 45.9 +/- 10.4 %HRR (entering a car). Physical strain could be notably high, but large variations among subjects were present. During all tasks, subjects with tetraplegia had significantly higher levels of strain than subjects with low (T6-L5) lesions. Physical strain was inversely related to parameters of physical capacity: isometric strength (r: -0.34 to -0.72), sprint power (r: -0.34 to -0.69), peak oxygen uptake (r: -0.41 to -0.81) and maximal power output (r: -0.52 to -0.82). Parameters of physical capacity were better predictors of physical strain than was the lesion level, and explained 37-71% of the variance in strain during ADL. It was also concluded that the method used in this study provides a quantitative and objective estimation of physical strain and may therefore be a useful tool to identify task difficulty during rehabilitation and to evaluate the results of task and physical training on the physical strain during ADL