Autoregulation is a process that is used to manipulate training based primarily on the measurement of an individual’s performance or their perceived capability to perform. Despite being established as a training framework since the 1940s, there has been limited systematic research investigating its broad utility. Instead, researchers have focused on disparate practices that can be considered specific examples of the broader autoregulation training framework. A primary limitation of previous research includes inconsistent use of key terminology (e.g., adaptation, readiness, fatigue, and response) and associated ambiguity of how to implement different autoregulation strategies. Crucially, this ambiguity in terminology and failure to provide a holistic overview of autoregulation limits the synthesis of existing research findings and their dissemination to practitioners working in both performance and health contexts. Therefore, the purpose of the current review was threefold: first, we provide a broad overview of various autoregulation strategies and their development in both research and practice whilst highlighting the inconsistencies in definitions and terminology that currently exist. Second, we present an overarching conceptual framework that can be used to generate operational definitions and contextualise autoregulation within broader training theory. Finally, we show how previous definitions of autoregulation fit within the proposed framework and provide specific examples of how common practices may be viewed, highlighting their individual subtleties.

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The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30–60% 1RM) + back squat (70–100% 1RM)) or back squat only (0 load, 30–100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/ηp2), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; ηp2 = 0.90) and back squat (p = 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ηp2 = 0.67–0.80), but not quadratic (p = 0.632–0.929, ηp2 = 0.001–0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

The Wellington Cancer Centre is equipped with two matched linear accelerators (Varian 2100CD) linear accelerators (identified as SN1027 and SN42). Each morning, before treatments commence, a radiation therapist carries out an output constancy check of the radiation output and every fortnight a physicist measures, in a phantom, the delivered radiation dose to check on the machine calibration. The daily output checks have been recorded into a database (Argus QA for Radiation Oncology) since August 1997 and in June 1998 the fortnightly calibration measurements were added. The information in the database, up to April 2003, has been analysed to consider the quality of the daily constancy checks as compared with the fortnightly calibration measurements and whether the data contains useful information on machine performance. After allowance for the effects of machine recalibration the fortnightly calibration measurements had an average standard deviation of 0.4% and the daily constancy checks 0.8%. The daily constancy checks had a greater number of large deviations than would be expected assuming a normal distribution and were not a good predictor of the need for a recalibration. The fortnightly calibration measurements with a much lower spread give a reliable indication of the need for a recalibration allowing the adoption of a +/- 1% tolerance. Over the period analysed one accelerator (SN42) was relatively stable with the output generally drifting between +/- 1% while the other (SN1027) had a consistent increase in the average output of about 2.5% per year.

Objective: The objective of this scoping review was to examine and map the evidence relating to the reporting and evaluation of technologies for the prevention and detection of falls in adult hospital inpatients.Introduction: Falls are a common cause of accidental injury, leading to a significant safety issue in hospitals globally, and resulting in substantial human and economic costs. Previous research has focused on community settings with less emphasis on hospital settings to date. Inclusion criteria: Participants included adult inpatients, aged 18 years and over; Concept included the use of fall prevention or detection technologies; Context included any hospital ward setting. Methods: This scoping review was conducted according to Joanna Briggs Institute (JBI) methodology for scoping reviews, guided by an a-priori protocol. A wide selection of databases including Medline, CINAHL, AMED, EmBASE, PEDro, Epistimonikos, and Science Direct were searched for records from inception to October 2019. Other sources included grey literature, trial registers, government health department websites and websites of professional bodies. Only studies in the English language were included. A three-step search strategy was employed with all records exported for subsequent title and abstract screening, prior to full text screening. Screening was performed by two independent reviewers and data extraction by one reviewer following agreement checks. Data is presented in narrative and tabular form.Results: Over 13,000 records were identified with 404 included in the scoping review: 336 reported on fall prevention technologies, 51 targeted detection and 17 concerned both. The largest contributions of studies came from the USA (n=185), Australia (n=65), UK (n=36) and Canada (n=18).There was a variety of study designs including 77 prospective cohort studies, 33 before-after studies and a large number of systematic reviews (n=35). However, relatively few randomised controlled trials were conducted (n=25). The majority of records reported on multifactorial and multicomponent technologies (n=178), followed by fall detection devices (n=86). Few studies reported on the following interventions in isolation: fall risk assessment (n=6), environment design (n=8), sitters (n=5), rounding (n=3), exercise (n=3), medical/pharmaceutical (n=2), physiotherapy (n=1) and nutritional (n=1). The majority (56%) of studies reported clinical effectiveness outcomes with smaller numbers (14%) reporting feasibility and/or acceptability outcomes, or cost-effectiveness outcomes (5%). Conclusions:This review has mapped the literature on falls prevention and detection technology and outcomes for adults in the hospital setting. Despite the volume of available literature, there remains a need for further high-quality research on fall prevention and detection technologies.