Effect of Grade III Lumbar Mobilization on Back Muscles in Chronic Low Back Pain: A Randomized Controlled Trial

BACKGROUND: Lumbar mobilization is a standard intervention for lower back pain (LBP). However, its effect on the activity of back muscles is not well known. OBJECTIVES: To investigate the effects of lumbar mobilization on the activity/contraction of erector spinae (ES) and lumbar multifidus (LM) muscles in people with LBP. DESIGN: Randomized controlled study. METHODS: 21 subjects with LBP received either grade III central lumbar mobilization or placebo (light touch) intervention on lumbar segment level 4 (L4). Surface electromyography (EMG) signals of ES and ultrasound (US) images of LM were captured before and after the intervention. The contraction of LM was calculated from US images at L4 level. The normalized amplitude of EMG signals (nEMG) and activity onset of ES were calculated from the EMG signals at both L1 and L4 levels. RESULTS: Significant differences were found between the mobilization and placebo groups in LM contraction (p=0.03), nEMG of ES at L1 (p=0.01) and L4 (p=0.05), and activity onset of ES at L1 (p=0.02). CONCLUSION: Lumbar mobilization decreased both the activity amplitude and the activity onset of ES in people with LBP. However, the significant difference in LM contraction was small and may not have clinical significance

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

Differences in adolescent activity and dietary behaviors across home, school, and other locations warrant location-specific intervention approaches

Background Investigation of physical activity and dietary behaviors across locations can inform “setting-specific” health behavior interventions and improve understanding of contextual vulnerabilities to poor health. This study examined how physical activity, sedentary time, and dietary behaviors differed across home, school, and other locations in young adolescents. Methods Participants were adolescents aged 12–16 years from the Baltimore-Washington, DC and the Seattle areas from a larger cross-sectional study. Participants (n = 472) wore an accelerometer and Global Positioning Systems (GPS) tracker (Mean days = 5.12, SD = 1.62) to collect location-based physical activity and sedentary data. Participants (n = 789) completed 24-h dietary recalls to assess dietary behaviors and eating locations. Spatial analyses were performed to classify daily physical activity, sedentary time patterns, and dietary behaviors by location, categorized as home, school, and “other” locations. Results Adolescents were least physically active at home (2.5 min/hour of wear time) and school (2.9 min/hour of wear time) compared to “other” locations (5.9 min/hour of wear time). Participants spent a slightly greater proportion of wear time in sedentary time when at school (41 min/hour of wear time) than at home (39 min/hour of wear time), and time in bouts lasting ≥30 min (10 min/hour of wear time) and mean sedentary bout duration (5 min) were highest at school. About 61% of daily energy intake occurred at home, 25% at school, and 14% at “other” locations. Proportionately to energy intake, daily added sugar intake (5 g/100 kcal), fruits and vegetables (0.16 servings/100 kcal), high calorie beverages (0.09 beverages/100 kcal), whole grains (0.04 servings/100 kcal), grams of fiber (0.65 g/100 kcal), and calories of fat (33 kcal/100 kcal) and saturated fat (12 kcal/100 kcal) consumed were nutritionally least favorable at “other” locations. Daily sweet and savory snacks consumed was highest at school (0.14 snacks/100 kcal). Conclusions Adolescents’ health behaviors differed based on the location/environment they were in. Although dietary behaviors were generally more favorable in the home and school locations, physical activity was generally low and sedentary time was higher in these locations. Health behavior interventions that address the multiple locations in which adolescents spend time and use location-specific behavior change strategies should be explored to optimize health behaviors in each location

Quantization Points of Densities and Samples

Representing a continuous random variable by a finite number of values is known as quantization. Given a probability density function (p.d.f.) or a random sample, the quantization problem is to choose those k values (or points) that will best represent the given p.d.f. or sample. The optimal quanitization points (also known as principal points or representative points) are those that minimize loss of information, usually measured in terms of mean rth power absolute error. When mean square error is used, principal points occur at the conditional means of the regions they represent. Principal points of location-scale family densities can be found using a simple linear transformation. If the p.d.f. of a random variable is symmetric and strongly unimodal, the variable has a unique set of two principal points, and the points are symmetric about the mean. We outline two algorithms for finding principal points for random variables with known densities. We discuss asymptotically optimal quanizers. These quantizers are easier to find than optimal quantizers and perform well for moderate to large values of k. Representative points can be estimated for sample data. We discuss both parametric and non-parametric cases. Quantizer mismatch occurs when a quantizer is based on one density and the random variable has a different density. Given a random sample, we can estimate the underlying density and find representative points based on this estimated density, or we can use an algorithm to estimate a set of representative points. We introduce a closest pair algorithm that identifies clusters in the sample and uses these clusters to estimate representative points. Given a sample of size n, the algorithm provides a set of k points in n - k iterations. We use the algorithm to find points for three samples and state the results. Several measures of quantizer performance are also discussed