The objective of the present study was to determine if there is a sex-based difference in lifting technique across increasing-load conditions. Eleven male and 14 female participants (n = 25) with no previous history of low back disorder participated in the study. Participants completed freestyle, symmetric lifts of a box with handles from the floor to a table positioned at 50% of their height for five trials under three load conditions (10%, 20%, and 30% of their individual maximum isometric back strength). Joint kinematic data for the ankle, knee, hip, and lumbar and thoracic spine were collected using a two-camera Optotrak motion capture system. Joint angles were calculated using a three-dimensional Euler rotation sequence. Principal component analysis (PCA) and single component reconstruction were applied to assess differences in lifting technique across the entire waveforms. Thirty-two PCs were retained from the five joints and three axes in accordance with the 90% trace criterion. Repeated-measures ANOVA with a mixed design revealed no significant effect of sex for any of the PCs. This is contrary to previous research that used discrete points on the lifting curve to analyze sex-based differences, but agrees with more recent research using more complex analysis techniques. There was a significant effect of load on lifting technique for five PCs of the lower limb (PC1 of ankle flexion, knee flexion, and knee adduction, as well as PC2 and PC3 of hip flexion) (p
There are a vast number of smartphone applications (apps) aimed at promoting medication adherence on the market; however, the theory and evidence base in terms of applying established health behavior change techniques underpinning these apps remains unclear. This study aimed to code these apps using the Behavior Change Technique Taxonomy (v1) for the presence or absence of established behavior change techniques. The sample of apps was identified through systematic searches in both the Google Play Store and Apple App Store in February 2015. All apps that fell into the search categories were downloaded for analysis. The downloaded apps were screened with exclusion criteria, and suitable apps were reviewed and coded for behavior change techniques in March 2015. Two researchers performed coding independently. In total, 166 medication adherence apps were identified and coded. The number of behavior change techniques contained in an app ranged from zero to seven (mean=2.77). A total of 12 of a possible 96 behavior change techniques were found to be present across apps. The most commonly included behavior change techniques were "action planning" and "prompt/cues," which were included in 96% of apps, followed by "self-monitoring" (37%) and "feedback on behavior" (36%). The current extent to which established behavior change techniques are used in medication adherence apps is limited. The development of medication adherence apps may not have benefited from advances in the theory and practice of health behavior change. Copyright 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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