Hand-held dynamometry (HHD) has never previously been used to examine isometric muscle power. Rate of force development (RFD) is often used for muscle power assessment, however no consensus currently exists on the most appropriate method of calculation. The aim of this study was to examine the reliability of different algorithms for RFD calculation and to examine the intra-rater, inter-rater, and inter-device reliability of HHD as well as the concurrent validity of HHD for the assessment of isometric lower limb muscle strength and power. 30 healthy young adults (age: 23±5yrs, male: 15) were assessed on two sessions. Isometric muscle strength and power were measured using peak force and RFD respectively using two HHDs (Lafayette Model-01165 and Hoggan microFET2) and a criterion-reference KinCom dynamometer. Statistical analysis of reliability and validity comprised intraclass correlation coefficients (ICC), Pearson correlations, concordance correlations, standard error of measurement, and minimal detectable change. Comparison of RFD methods revealed that a peak 200ms moving window algorithm provided optimal reliability results. Intra-rater, inter-rater, and inter-device reliability analysis of peak force and RFD revealed mostly good to excellent reliability (coefficients ≥ 0.70) for all muscle groups. Concurrent validity analysis showed moderate to excellent relationships between HHD and fixed dynamometry for the hip and knee (ICCs ≥ 0.70) for both peak force and RFD, with mostly poor to good results shown for the ankle muscles (ICCs = 0.31–0.79). Hand-held dynamometry has good to excellent reliability and validity for most measures of isometric lower limb strength and power in a healthy population, particularly for proximal muscle groups. To aid implementation we have created freely available software to extract these variables from data stored on the Lafayette device. Future research should examine the reliability and validity of these variables in clinical populations.

Falls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12 months following rehabilitation discharge. A prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63 years; median 24 days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars. Over 12 months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P < 0.001–0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P < 0.040; IQR-odds ratio(OR) = 1.37–7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P = 0.001–0.018; IQR-OR = 5.28–10.29). Reduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

Synopsis: This clinical commentary discusses the mechanisms used by the lower-limb musculature to achieve faster running speeds. A variety of methodological approaches have been taken to evaluate lower-limb muscle function during running, including direct recordings of muscle electromyographic signal, inverse dynamics-based analyses, and computational musculoskeletal modeling. Progressing running speed from jogging to sprinting is mostly dependent on ankle and hip muscle performance. For speeds up to approximately 7.0 m/s, the dominant strategy is to push on the ground forcefully to increase stride length, and the major ankle plantar flexors (soleus and gastrocnemius) have a particularly important role in this regard. At speeds beyond approximately 7.0 m/s, the force-generating capacity of these muscles becomes less effective. Therefore, as running speed is progressed toward sprinting, the dominant strategy shifts toward the goal of increasing stride frequency and pushing on the ground more frequently. This strategy is achieved by generating substantially more power at the hip joint, thereby increasing the biomechanical demand on proximal lower-limb muscles such as the iliopsoas, gluteus maximus, rectus femoris, and hamstrings. Basic science knowledge regarding lower-limb muscle function during running has implications for understanding why sprinting performance declines with age. It is also of great value to the clinician for designing rehabilitation programs to restore running ability in young, previously active adults who have sustained a traumatic brain injury and have severe impairments of muscle function (eg, weakness, spasticity, poor motor control) that limit their capacity to run at any speed. J Orthop Sports Phys Ther 2014;44(10):813–824. Epub 7 August 2014. doi:10.2519/jospt.2014.5433

The objective of this study was to evaluate the effectiveness of individual cognitive behavioural therapy (CBT) for post-stroke fatigue and sleep disturbance compared to treatment as usual (TAU). In a parallel two-group pilot randomised controlled trial of 15 participants, nine were allocated to eight weekly sessions of adapted CBT and six continued usual care rehabilitation. The primary outcome was the Fatigue Severity Scale (FSS-7) at two and four months from baseline. Secondary outcomes included measures of sleep, mood and quality of life. Outcomes were assessed by a rater who was blind to group membership. At the four-month endpoint, the CBT group demonstrated significantly reduced fatigue relative to TAU (FSS-7 mean difference: 1.92, 95% CI: 0.24 to 3.60). Significant group differences also emerged for sleep quality and depression, favouring the CBT group. Insomnia and physical quality of life improved immediately post-therapy but were no longer superior to TAU at follow-up. Overall, CBT is a promising treatment for improving post-stroke fatigue, sleep quality and depression. Gains were maintained for two months after therapy cessation and represented large treatment effects. These findings highlight the feasibility of the intervention and warrant extension to a phase III clinical trial.