Neuromuscular training improves movement competency and physical performance measures in 11-13 year old female netball athletes

The purpose of this study was to examine the effects of a neuromuscular training program on movement competency and measures of physical performance in youth female netball players. It was hypothesized that significant improvements would be found in movement competency and physical performance measures following the intervention. Twenty-three junior female netball players (age, 12.17 ± 0.94 yrs; height, 1.63 ± 0.08 m; weight, 51.81 ± 8.45 kg) completed a test battery before and after a six-week training intervention. 13 of these athletes underwent six weeks of neuromuscular training, which incorporated plyometrics and resistance training. Trained athletes showed significant improvements in 20 m sprint time, 505 agility time, countermovement jump height and peak power (p ≤ 0.05, g \u3e 0.8). Additionally, trained athletes significantly improved their score in the Netball Movement Screening Tool (NMST) (p \u3c 0.05, g \u3e -1.30); while the athletes also demonstrated increased reach in the anterior and posteromedial directions for the right and left leg, and in the posterolateral direction for the left leg only in the Star Excursion Balance Test (SEBT) (p \u3c 0.05, g \u3e -0.03). Control subjects did not exhibit any significant changes during the 6-week period. Significant negative correlations were found between improved score on the NMST and decreased 5 m, 10 m and 20 m sprint time, and 505 change of direction time (r \u3e 0.4, p ≤ 0.05). Results of the study affirm the hypothesis that a six-week neuromuscular training intervention can improve performance and movement competency in youth netball player

Cluster Training: A Novel Method for Introducing Training Program Variation

The introduction of novel training stimuli plays a crucial role in inducing specific training adaptations. One method that can be employed to introduce a novel stimulus to the training program while maximizing the velocity and power output of the training exercise is the inclusion of the cluster set configuration. The current review presents the theoretical and research foundation for the use of the cluster set in periodized training programs and offers examples of practical applications that can be used in the preparation of athletes in a variety of sports

Peak Force and Rate of Force Development During Isometric Mid-Thigh Clean Pulls and Dynamic Mid-Thigh Clean Pulls Performed at Various Intensities

Eight male collegiate weightlifters (age: 21.2 ± 0.9 years; height: 177.6 ± 2.3 cm; and body mass: 85.1 ± 3.3 kg) participated in this study to compare isometric to dynamic force-time dependent variables. Subjects performed the isometric and dynamic mid-thigh clean pulls at 30–120% of their one repetition maximum (1RM) power clean (118.4 ± 5.5 kg) on a 61 X 121.9–cm AMTI forceplate. Variables such as peak force (PF) and peak rate of force development (PRFD) were calculated and were compared between isometric and dynamic conditions. The relationships between force-time dependent variables and vertical jump performances also were examined. The data indicate that the isometric PF had no significant correlations with the dynamic PF against light loads. On the one hand, there was a general trend toward stronger relationships between the isometric and dynamic PF as the external load increased for dynamic muscle actions. On the other hand, the isometric and dynamic PRFD had no significant correlations regardless of the external load used for dynamic testing. In addition, the isometric PF and dynamic PRFD were shown to be strongly correlated with vertical jump performances, whereas the isometric PRFD and dynamic PF had no significant correlations with vertical jump performances. In conclusion, it appears that the isometric and dynamic measures of force-time curve characteristics represent relatively specific qualities, especially when dynamic testing involves small external loads. Additionally, the results suggest that athletes who possess greater isometric maximum strength and dynamic explosive strength tend to be able to jump higher. Eight male collegiate weightlifters (age: 21.2 ± 0.9 years; height: 177.6 ± 2.3 cm; and body mass: 85.1 ± 3.3 kg) participated in this study to compare isometric to dynamic force-time dependent variables. Subjects performed the isometric and dynamic mid-thigh clean pulls at 30–120% of their one repetition maximum (1RM) power clean (118.4 ± 5.5 kg) on a 61 X 121.9–cm AMTI forceplate. Variables such as peak force (PF) and peak rate of force development (PRFD) were calculated and were compared between isometric and dynamic conditions. The relationships between force-time dependent variables and vertical jump performances also were examined. The data indicate that the isometric PF had no significant correlations with the dynamic PF against light loads. On the one hand, there was a general trend toward stronger relationships between the isometric and dynamic PF as the external load increased for dynamic muscle actions. On the other hand, the isometric and dynamic PRFD had no significant correlations regardless of the external load used for dynamic testing. In addition, the isometric PF and dynamic PRFD were shown to be strongly correlated with vertical jump performances, whereas the isometric PRFD and dynamic PF had no significant correlations with vertical jump performances. In conclusion, it appears that the isometric and dynamic measures of force-time curve characteristics represent relatively specific qualities, especially when dynamic testing involves small external loads. Additionally, the results suggest that athletes who possess greater isometric maximum strength and dynamic explosive strength tend to be able to jump higher

REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): Study protocol of a pragmatic randomised trial

Introduction Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals\u27 patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. Methods and analysis In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial\u27s primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. Ethics and dissemination This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences. Trial registration number Clinicaltrials.gov (NCT04473326)