Evaluation of an organisational-level monetary incentive to promote the health and wellbeing of workers in small and medium-sized enterprises: A mixed-methods cluster randomised controlled trial.

We conducted an independent evaluation on the effectiveness of an organisational-level monetary incentive to encourage small and medium-sized enterprises (SMEs) to improve employees' health and wellbeing. This was A mixed-methods cluster randomised trial with four arms: high monetary incentive, low monetary incentive, and two no monetary incentive controls (with or without baseline measurements to examine 'reactivity' The consequence of particpant awareness of being studied, and potential impact on participant behavior effects). SMEs with 10-250 staff based in West Midlands, England were eligible. We randomly selected up to 15 employees at baseline and 11 months post-intervention. We elicited employee perceptions of employers' actions to improve health and wellbeing; and employees' self-reported health behaviours and wellbeing. We also interviewed employers and obtained qualitative data. One hundred and fifty-two SMEs were recruited. Baseline assessments were conducted in 85 SMEs in three arms, and endline assessments in 100 SMEs across all four arms. The percentage of employees perceiving "positive action" by their employer increased after intervention (5 percentage points, pp [95% Credible Interval -3, 21] and 3pp [-9, 17], in models for high and low incentive groups). Across six secondary questions about specific issues the results were strongly and consistently positive, especially for the high incentive. This was consistent with qualitative data and quantitative employer interviews. However, there was no evidence of any impact on employee health behaviour or wellbeing outcomes, nor evidence of 'reactivity'. An organisational intervention (a monetary incentive) changed employee perceptions of employer behaviour but did not translate into changes in employees' self-reports of their own health behaviours or wellbeing. Trial registration: AEARCTR-0003420, registration date: 17.10.2018, retrospectively registered (delays in contracts and identfying a suitable trial registry). The authors confirm that there are no ongoing and related trials for this intervention.

Parameter identification and sedative sensitivity analysis of an agitation-sedation model.

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation dynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. In this research, the agitation-sedation model parameters are identified using an integral-based fitting method developed in this work. Parameter variance is then analysed over 37 intensive care unit patients. The parameter identification method is shown to be effective and computationally inexpensive, making it suited to real-time clinical control applications. Sedative sensitivity, an important model parameter, is found to be both patient-specific and time-varying. However, while the variation between patients is observed to be as large as a factor 10, the observed variation in time is smaller, and varies slowly over a period of days rather than hours. The high fitted model performance across all patients show that the agitation-sedation model presented captures the fundamental dynamics of the agitation-sedation system. Overall, these results provide additional insight into the system and clinical dynamics of sedation management.

A new model validation tool using kernel regression and density estimation.

In physiological system modelling for control or decision support, model validation is a critical element. A nonparametric approach for assessing the validity of deterministic dynamic models against empirical data is developed, based on kernel regression and kernel density estimation, yielding visual graphical assessment tools as well as numerical metrics of compatibility between the model and the data. Nonparametric regression has been suggested for assessing a parametric statistical model by constructing a confidence band for the proposed model and then checking whether the nonparametric regression curve lies within the band. However, for deterministic models, there is no confidence band that can be constructed. A reversal of roles is therefore suggested--construct a probability band for the nonparametric regression curve and check whether the proposed model lies within the band. This approach extends the utility of nonparametric regression for model assessment to deterministic models. Weighted kernel density estimation is incorporated to derive a density profile for the regression curve, creating a local graphical validation tool. In addition, the density profile is used to define and compute two numerical measures--average normalized density (AND) and relative average normalized density (RAND), representing global statistical validity measures. These tools are demonstrated using a biomedical system model for agitation-sedation and sedation management control.

Quantifying agitation in sedated ICU patients using digital imaging.

Agitation is a significant problem in the Intensive Care Unit (ICU), affecting 71% of sedated adult patients during 58% of ICU patient-days. Subjective scale based assessment-methods focused primarily on assessing excessive patient motion are currently used to assess the level of patient agitation, but are limited in their accuracy and resolution. This research quantifies this approach by developing an objective agitation measurement from patient motion that is sensed using digital video image processing. A fuzzy inference system (FIS) is developed to classify levels of motion that correlate with observed patient agitation, while accounting for motion due to medical staff working on the patient. Clinical tests for five ICU patients have been performed to verify the validity of this approach in comparison to agitation graded by nursing staff using the Riker Sedation-Agitation Scale (SAS). All trials were performed in the Christchurch Hospital Department of Intensive Care, with ethics approval from the Canterbury Ethics Committee. Results show good correlation with medical staff assessment with no false positive results during calm periods. Clinically, this initial agitation measurement method promises the ability to consistently and objectively quantify patient agitation to enable better management of sedation and agitation through optimised drug delivery leading to reduced length of stay and improved outcome.

Modeling and control of the agitation-sedation cycle for critical care patients.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, is damaging to patient health, and increases length of stay and healthcare costs. The mathematical model presented captures the essential dynamics of the agitation-sedation system for the first time, and is statistically validated using recorded infusion data for 37 patients. Constant patient-specific patient parameters are used, illustrating the commonality of these fundamental dynamics over a broad range of patients. The validated model serves as a basis for comparison of sedation administration methods, devices, therapeutics and protocols. Heavy derivative feedback control is shown to be an effective means of managing agitation, given consistent agitation measurement. The improved agitation management reduces the modeled mean and peak agitation levels 68.4% and 52.9% on average, respectively. Some patients showed over 90% reduction in mean agitation level through increased control gains. This improved agitation management is achieved via heavy derivative feedback control of sedation administration, which provides an essentially bolus-driven management approach, aligned with recent sedation practices.