Movement variability is defined as the normal variations in motor performance across multiple repetitions of a task. However, the term “movement variability” can mean different things depending on context, and when used by itself does not capture the specifics of what has been investigated. Within sport, complex movements are performed repeatedly under a variety of different constraints (e.g. different situations, presence of defenders, time pressure). Movement variability has implications for sport performance and injury risk management. Given the importance of movement variability, it is important to understand the terms used to measure and describe it. This broad term of “movement variability” does not specify the different types of movement variability that are currently being assessed in the sporting literature. We conducted a scoping review (1) to assess the current terms and definitions used to describe movement variability within sporting tasks and (2) to utilise the results of the review for a proposed framework that distinguishes and defines the different types of movement variability within sporting tasks. To be considered eligible, sources must have assessed a sporting movement or skill and had at least one quantifiable measure of movement variability. A total of 43 peer-reviewed journal article sources were included in the scoping review. A total of 280 terms relating to movement variability terminology were extracted using a data-charting form jointly developed by two reviewers. One source out of 43 (2%) supplied definitions for all types of movement variability discussed. Moreover, 169 of 280 terms (60%) were undefined in the source material. Our proposed theoretical framework explains three types of movement variability: strategic, execution, and outcome. Strategic variability describes the different approaches or methods of movement used to complete a task. Execution variability describes the intentional and unintentional adjustments of the body between repetitions within the same strategy. Outcome variability describes the differences in the result or product of a movement. These types emerged from broader frameworks in motor control and were adapted to fit the movement variability needs in sports literature. By providing specific terms with explicit definitions, our proposed framework can ensure like-to-like comparisons of previous terms used in the literature. The practical goal of this framework is to aid athletes, coaches, and support staff to gain a better understanding of how the different types of movement variability within sporting tasks contribute to performance. The framework may allow training methods to be tailored to optimise the specific aspects of movement variability that contribute to success. This review was retrospectively registered using the Open Science Framework (OSF) Registries (https://osf.io/q73fd).

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT have the potential to reduce the human workload of the SR process while maintaining accuracy. We propose a new hybrid methodology that combines the strengths of LLMs and humans using the ability of LLMs to summarize large bodies of text autonomously and extract key information. This is then used by a researcher to make inclusion/exclusion decisions quickly. This process replaces the typical manually performed title/abstract screening, full-text screening, and data extraction steps in an SR while keeping a human in the loop for quality control. We developed a semi-automated LLM-assisted (Gemini-Pro) workflow with a novel innovative prompt development strategy. This involves extracting three categories of information including identifier, verifier, and data field (IVD) from the formatted documents. We present a case study where our hybrid approach reduced errors compared with a human-only SR. The hybrid workflow improved the accuracy of the case study by identifying 6/390 (1.53%) articles that were misclassified by the human-only process. It also matched the human-only decisions completely regarding the rest of the 384 articles. Given the rapid advances in LLM technology, these results will undoubtedly improve over time.

Purpose: Repetitive task practice after stroke is important to improve function, yet adherence to exercise is low. The aim of this study was to determine whether using the internet, a tablet application, and a chair sensor that connected to a therapist was feasible in monitoring adherence and progressing a functional exercise at home. Methods: Ten participants with stroke completed a 4-week sit-to-stand exercise using the technology at home (ACTRN12616000051448). A therapist remotely monitored exercise adherence, progressed goals, and provided feedback via the app. Measures of feasibility (design, recruitment/withdrawals, adherence, safety, participant satisfaction and estimates of effect on function) were collected. Results: Participants' mean age was 73.6 years [SD 9.9 years]. The system was feasible to deliver and monitor exercise remotely. All participants completed the study performing a mean 125% of prescribed sessions and 104% of prescribed repetitions. Participants rated the system usability (78%), enjoyment (70%) and system benefit (80%) as high. No adverse events were reported. The mean pre- and post-intervention difference in the total short performance physical battery score was 1.4 (95% CI 0.79, 2.00). Conclusions: It was feasible and safe to prescribe and monitor exercises using an app and sensor-based system. A definitive trial will determine whether such technology could facilitate greater exercise participation after stroke.

Sedentary time is prevalent following stroke, limiting functional improvement, and increasing cardiovascular risk. At discharge we examined: 1) change in sedentary time and activity over the follow...