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to develop LBP as the length of time on their feet increases. 15 From an occupal ow back pain (LBP) is a major contributor to increasing healthcare costs in North America, with estimates that 70% to 85% of all adults will experience an acute episode of LBP at some point in their lives. 8 Epidemiological studies have shown that standing occupations have a strong association with LBP.1,25 Checkout clerks and other individuals with occupations requiring long periods of standing are known t STudy deSign: Analytic observational prospective study performed in a controlled laboratory setting.t obJecTiveS: To assess the ability of a new screening tool, the active hip abduction test, to predict low back pain development during prolonged standing in previously asymptomatic individuals.t background: Most screening tools used for a patient with low back pain do not assess the patient's ability to maintain postural control in the frontal plane, when placed in an unstable position. Postural-control differences in pain developers, as compared to non-pain developers, during standing have been found previously. An attempt was made to predict pain development with a simple screening test.t meThodS: Forty-three previously asymptomatic volunteers underwent a clinical assessment prior to a 2-hour standing protocol designed to induce low back pain. Participants rated low back pain with a visual analog scale and were classified into pain developers or non-pain developers.t reSulTS: Forty percent of participants developed low back pain. The active hip abduction test was the only test that discriminated between pain-developer groups. When the examiner scored the test, the odds ratio was 3.85 (95% confidence interval [CI]: 1.05-19.07), and when the test was self-rated, the odds ratio was 6.55 (95% CI: 1.14-37.75) for pain development during standing.t concluSion: The active hip abduction test appears to show promise for predicting individuals who are at risk for low back pain development during prolonged standing. More work is required to validate the test in clinical populations, and to assess interrater and intrarater reliability. t level oF evidence: Diagnosis, level 2b. Ther 2009;39(9):649-657. doi:10.2519/jospt.2009 t key wordS: clinical assessment, diagnostic tests, lumbar spine, stabilization tional safety and health perspective, it would be ideal to have a simple screening tool that could identify "at-risk" workers and guide an appropriate preventative exercise program. J Orthop Sports PhysWhile multiple researchers have identified differences in motor control patterns between patients with LBP and healthy controls, 4,5,7 there have been few prospective studies published to evaluate whether these differences are adaptive to the LBP disorder or are risk factors that might increase the likelihood of pain development. We have previously used a "functionally induced low back pain" model as a prospective design to study factors linked to LBP development during standing. 9,10,23 The main idea behind this protocol is that a percentage ...

uman movement and rehabilitation scientists are often faced with the daunting task of quantifying relationships between variables in large datasets. Powerful and efficient, yet relatively easy-to-apply tools, are autocorrelation and cross-correlation analyses for quantifying associations between variables. Based upon the Pearson product moment correlation, autocorrelation and crossTechnical note.To provide background theory and information and to describe relevant applications of autocorrelation and cross-correlation methodology as they apply to the field of motor control in human movement and rehabilitation research.Commonly used methodologies for pattern and event recognition, determination of muscle activation timing for investigation of movement coordination, and motor control are generally difficult to implement, particularly with large datasets. A brief description of the underlying mathematical theory of correlation analyses is given, followed by 4 different examples of how this methodology is useful for research in the movement sciences.Examples demonstrating the utility of correlation analyses are presented from several different studies conducted at the University of Waterloo.Autocorrelation was used to demonstrate the presence of 60-Hz noise in an electromyography signal that was not visible in the raw data. A "top-down" paraspinal muscle activation pattern was demonstrated for healthy adults during gait, with the use of cross-correlation. Cross-correlation was also used to quantify coactivation of bilateral gluteus medius muscles during standing in individuals who developed low-back pain. Gender differences in gluteus medius control of mediolateral center of pressure were seen with the use of cross-correlation.Autocorrelation and crosscorrelation have been shown to be an effective tool for several different applications in the movement sciences. Examples of the method's utility include noise detection within a signal, determination of relative muscle activation onsets for postural control, objective quantification of muscle coactivation, and relating muscle activations with mechanical events.

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Falls are a leading contributor to disability in older adults. Increased muscle co-contraction in the lower extremities during static and dynamic balance challenges has been associated with aging, and also with a history of falling. Co-contraction during static balance challenges has not been previously linked with performance on clinical tests designed to ascertain fall risk. The purpose of this study was to investigate the relationship between co-contraction about the ankle during static balance challenges with fall risk on a commonly used dynamic balance assessment, the Four Square Step Test (FSST). Twenty-three volunteers (mean age 73 years) performed a series of five static balance challenges (Romberg eyes open/closed, Sharpened Romberg eyes open/closed, and Single Leg Standing) with continuous electromyography (EMG) of bilateral tibialis anterior and gastrocnemius muscles. Participants then completed the FSST and were categorized as 'at-risk' or 'not-at-risk' to fall based on a cutoff time of 12 s. Co-contraction was quantified with co-contraction index (CCI). CCI during narrow base conditions was positively correlated with time to complete FSST. High CCIs during all static balance challenges with the exception of Romberg stance with eyes closed were predictive of being at-risk to fall based on FSST time, odds ratio 19.3. The authors conclude that co-contraction about the ankle during static balance challenges can be predictive of performance on a dynamic balance test.