Measuring joint range of motion is an important skill for many allied health professionals. While the Universal Goniometer is the most commonly utilised clinical tool for measuring joint range of motion, the evolution of smartphone technology and applications (apps) provides the clinician with more measurement options. However, the reliability and validity of these smartphones and apps is still somewhat uncertain. The aim of this study was to systematically review the literature regarding the intra- and inter-rater reliability and validity of smartphones and apps to measure joint range of motion. Eligible studies were published in English peer-reviewed journals with full text available, involving the assessment of reliability and/or validity of a non-videographic smartphone app to measure joint range of motion in participants >18 years old. An electronic search using PubMed, Medline via Ovid, EMBASE, CINAHL, and SPORTSDiscus was performed. The risk of bias was assessed using a standardised appraisal tool. Twenty-three of the eligible 25 studies exceeded the minimum 60% score to be classified as a low risk of bias, although 3 of the 13 criteria were not achieved in >50% of the studies. Most of the studies demonstrated adequate intra-rater or inter-rater reliability and/or validity for >50% of the range of motion tests across all joints assessed. However, this level of evidence appeared weaker for absolute (e.g. mean difference ± limit of agreement, minimal detectable change) than relative (e.g. intraclass correlation, correlation) measures; and for spinal rotation than spinal extension, flexion and lateral flexion. Our results provide clinicians with sufficient evidence to support the use of smartphones and apps in place of goniometers to measure joint motion. Future research should address some methodological limitations of the literature, especially including the inclusion of absolute and not just relative reliability and validity statistics.

The aim of this systematic review was to evaluate the impact of bilaterally symmetrical backpack systems borne on the posterior trunk on walking biomechanics, as backpacks represent the most prevalent method of load carriage in the military and civilian population. A search of electronic databases was performed for studies that only investigated posteriorly-borne backpack carriage during level-grade walking (treadmill and over ground). Methodology of studies was assessed, and both meta-analysis and qualitative synthesis were completed. Fifty-four studies were included in this review. In summary, the available literature showed that backpack carriage in walking was associated with an increased trunk flexion angle, increased hip and ankle range of motion, increased vertical and horizontal ground reaction force, increased cadence, and reduced stride length. Several variations in backpack carriage protocols could explain between-study variations in results, including: walking speed, backpack carriage skill level, the use of a hip belt, and posterior displacement of the load away from the trunk. The findings of this systematic review will inform backpack carriage practices in the area of injury risk assessment and physical performance enhancement.

Background:In accordance with the principle of training specificity, adaptations to vertically-or horizontally-orientated plyometric training (VPT, HPT) directly transfer to athletic tasks that are carried out in the same direction as they are performed. Objectives:The objective of this systematic review and meta-analysis was to determine the relative effect of VPT and HPT on both vertical and horizontal measures of physical performance.

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Purpose To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. Methods Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. Results Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak = 0.047) in model 1, the deltoid muscle (peak = 0.052) in model 2, and the iliocostalis muscle (peak = 0.16) in model 3. Conclusion The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk.