Morning exercise and pre-breakfast metformin interact to reduce glycaemia in people with type 2 diabetes: a randomized crossover trial.

Exercise is recommended in the treatment of type 2 diabetes and can improve insulin sensitivity. However, previous evidence suggests that exercise at different times of the day in people with type 2 diabetes may have opposing outcomes on glycaemia. Metformin is the most commonly prescribed initial pharmacological intervention in type 2 diabetes, and may alter adaptions to exercise. It is unknown if there is an interaction between metformin and diurnal exercise outcomes. We aimed to investigate glycaemic outcomes of moderate intensity morning vs. evening exercise in people with type 2 diabetes being prescribed metformin monotherapy. In this study, nine males and nine females with type 2 diabetes undergoing metformin monotherapy (age 61 ± 8.2 years, mean ± SD) completed a 16-week crossover trial including 2-week baseline recording, 6 weeks randomly assigned to a morning exercise (07.00-10.00 h) or evening exercise (16.00-19.00 h) and a 2-week wash-out period. Exercise arms consisted of 30 min of walking at 70% of estimated max heart rate every other day. Glucose levels were measured with continuous glucose monitors and activity measured by wrist-worn monitors. Food-intake was recorded by 4-day food diaries during baseline, first and last 2 weeks of each exercise arm. There was no difference in exercise intensity, total caloric intake or total physical activity between morning and evening arms. As primary outcomes, acute (24 h) glucose area under the curve (AUC), was lower (P = 0.02) after acute morning exercise (180.6 ± 68.4 mmol/l) compared to baseline (210.3 ± 76.7 mmol/l); and there were no differences identified for glucose (mmol/l) between baseline, morning and evening exercise at any specific time point when data were analysed with two-way ANOVA. As secondary outcomes, acute glucose AUC was significantly lower (P = 0.01) in participants taking metformin before breakfast (152.5 ± 29.95 mmol/l) compared with participants taking metformin after breakfast (227.2 ± 61.51 mmol/l) only during the morning exercise arm; and during weeks 5-6 of the exercise protocol, glucose AUC was significantly lower (P = 0.04) for participants taking metformin before breakfast (168.8 ± 15.8 mmol/l), rather than after breakfast (224.5 ± 52.0 mmol/l), only during morning exercise. Our data reveal morning moderate exercise acutely lowers glucose levels in people with type 2 diabetes being prescribed metformin. This difference appears to be driven by individuals that consumed metformin prior to breakfast rather than after breakfast. This beneficial effect upon glucose levels of combined morning exercise and pre-breakfast metformin persisted through the final 2 weeks of the trial. Our findings suggest that morning moderate intensity exercise combined with pre-breakfast metformin intake may benefit the management of glycaemia in people with type 2 diabetes. KEY POINTS: Morning moderate exercise acutely lowers glucose levels in people with type 2 diabetes being prescribed metformin. This difference appears to be driven by individuals that consumed metformin prior to breakfast rather than after breakfast. Morning exercise combined with pre-breakfast metformin persistently reduced glucose compared to morning exercise combined with post-breakfast metformin through the final week (week 6) of the intervention. Our study suggests it may be possible to make simple changes to the time that people with type 2 diabetes take metformin and perform exercise to improve their blood glucose.

Care processes and outcomes of deprivation across the clinical course of kidney disease: findings from a high-income country with universal healthcare.

BACKGROUND: No single study contrasts the extent and consequences of inequity of kidney care across the clinical course of kidney disease. METHODS: This population study of Grampian (UK) followed incident presentations of acute kidney injury (AKI) and incident estimated glomerular filtration rate (eGFR) thresholds of <60, <45 and <30 mL/min/1.73 m2 in separate cohorts (2011-2021). The key exposure was area-level deprivation (lowest quintile of the Scottish Index of Multiple Deprivation). Outcomes were care processes (monitoring, prescribing, appointments, unscheduled care), long-term mortality and kidney failure. Modelling involved multivariable logistic regression, negative binomial regression and cause-specific Cox models with and without adjustment of comorbidities. RESULTS: There were 41 313, 51 190, 32 171 and 17 781 new presentations of AKI and eGFR thresholds <60, <45 and <30  mL/min/1.73 m2. A total of 6.1-7.8% of the population was from deprived areas and (versus all others) presented on average 5 years younger, with more diabetes and pulmonary and liver disease. Those from deprived areas were more likely to present initially in hospital, less likely to receive community monitoring, less likely to attend appointments and more likely to have an unplanned emergency department or hospital admission episode. Deprivation had the greatest association with long-term kidney failure at the eGFR <60 mL/min/1.73 m2 threshold {adjusted hazard ratio [HR] 1.48 [95% confidence interval (CI) 1.17-1.87]} and this association decreased with advancing disease severity [HR 1.09 (95% CI 0.93-1.28) at eGFR <30 mL/min/1.73 m2), with a similar pattern for mortality. Across all analyses the most detrimental associations of deprivation were an eGFR threshold <60 mL/min/1.73 m2, AKI, males and those <65 years of age. CONCLUSIONS: Even in a high-income country with universal healthcare, serious and consistent inequities in kidney care exist. The poorer care and outcomes with area-level deprivation were greater earlier in the disease course.

Healthcare applications of single camera markerless motion capture: a scoping review.

Background: Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology: Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results: A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions: Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

Improving predictor selection for injury modelling methods in male footballers.

OBJECTIVES: This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. METHODS: Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. RESULTS: The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. CONCLUSION: For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.

Model-Based Control of Individual Finger Movements for Prosthetic Hand Function.

Prosthetic devices for hand difference have advanced considerably in recent years, to the point where the mechanical dexterity of a state-of-the-art prosthetic hand approaches that of the natural hand. Control options for users, however, have not kept pace, meaning that the new devices are not used to their full potential. Promising developments in control technology reported in the literature have met with limited commercial and clinical success. We have previously described a biomechanical model of the hand that could be used for prosthesis control. The goal of this study was to evaluate the feasibility of this approach in terms of kinematic fidelity of model-predicted finger movement and the computational performance of the model. We show the performance of the model in replicating recorded hand and finger kinematics and find average correlations of 0.89 between modelled and recorded motions; we show that the computational performance of the simulations is fast enough to achieve real-time control with a robotic hand in the loop; and we describe the use of the model for controlling object gripping. Despite some limitations in accessing sufficient driving signals, the model performance shows promise as a controller for prosthetic hands when driven with recorded EMG signals. User-in-the-loop testing with amputees is necessary in future work to evaluate the suitability of available driving signals, and to examine translation of offline results to online performance.

The Stabilizing Function of Superficial Shoulder Muscles Changes Between Single-Plane Elevation and Reaching Tasks.

OBJECTIVE: The goal of this study was to determine whether and how much the stabilizing role of the shoulder muscles changes as a function of humeral elevation and the plane of elevation. METHODS: A musculoskeletal model, comprising a personalized scapulohumeral rhythm, was used to calculate the ratio of shear over compressive force (stability ratio) of three rotator cuff muscles (supraspinatus, infraspinatus and subscapularis) and three superficial shoulder muscles (middle deltoid, clavicular part of pectoralis major and latissimus dorsi) during abduction, flexion and reaching movements in 10 healthy adults. RESULTS: The range of the stability ratios was [Formula: see text] for the rotator cuff muscles compared to [Formula: see text] for the superficial shoulder muscles. In the superior-inferior direction, the stability ratios of all muscles changed with humeral elevation and for infraspinatus, subscapularis, latissimus dorsi and deltoid also with the plane of elevation. In the anterior-posterior direction, the stability ratios of all muscles changed with humeral elevation, except for the deltoid, and with the plane of elevation, except for the supraspinatus, with interaction effects in all muscles. CONCLUSION: The rotator cuff muscles provide greater compression than shear forces during all tasks. The stabilizing function of the superficial shoulder muscles examined in this study varies during tasks. SIGNIFICANCE: The findings can be used to predict in which movements the shoulder joint becomes more unstable and can be applied to understand how shear and compressive forces change in populations with abnormal shoulder motion.

Study of the measurement and predictive validity of the Functional Movement Screen.

OBJECTIVE: The aim of the study was to evaluate the reported measurement capabilities and predictive validity of the Functional Movement Screen (FMS) for injury. METHODS: This was a prospective observational longitudinal study of 24 male footballers from a single team in England, alongside analysis of an existing database over one season (September 2015-May 2016). A preseason FMS was carried out with scores recorded by an experienced assessor and derived, retrospectively, from the three-dimensional movement data that were simultaneously captured. The assessor scores were compared with the photogrammetric system to determine measurement validity, and predictive validity was quantified by assessing sensitivity and specificity (cut-off score of 14). RESULTS: The real-time assessor score matched the photogrammetric score awarded for one of the participants, was higher than the photogrammetric system for 22 participants and was lower than the photogrammetric system in 1 participant. There was no discernible relationship between FMS scores and the competencies required to be met as per the rules articulated for the allocation of a score. A higher number of total injuries were associated with higher FMS scores, whether determined through real-time assessment or codification of kinematic variables. Additionally, neither method of score determination was able to prospectively identify players at risk of serious injury. CONCLUSION: The FMS does not demonstrate the properties essential to be considered as a measurement scale and has neither measurement nor predictive validity. A possible reason for these observations could be the complexity in the instructions associated with the scale. Further work on eliminating redundancies and improving the measurement properties is recommended.

Real-time simulation of hand motion for prosthesis control.

Individuals with hand amputation suffer substantial loss of independence. Performance of sophisticated prostheses is limited by the ability to control them. To achieve natural and simultaneous control of all wrist and hand motions, we propose to use real-time biomechanical simulation to map between residual EMG and motions of the intact hand. Here we describe a musculoskeletal model of the hand using only extrinsic muscles to determine whether real-time performance is possible. Simulation is 1.3 times faster than real time, but the model is locally unstable. Methods are discussed to increase stability and make this approach suitable for prosthesis control.

Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

An optimized proportional-derivative controller for the human upper extremity with gravity.

When Functional Electrical Stimulation (FES) is used to restore movement in subjects with spinal cord injury (SCI), muscle stimulation patterns should be selected to generate accurate and efficient movements. Ideally, the controller for such a neuroprosthesis will have the simplest architecture possible, to facilitate translation into a clinical setting. In this study, we used the simulated annealing algorithm to optimize two proportional-derivative (PD) feedback controller gain sets for a 3-dimensional arm model that includes musculoskeletal dynamics and has 5 degrees of freedom and 22 muscles, performing goal-oriented reaching movements. Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations. After optimization, gain performance was evaluated on the basis of accuracy and efficiency of reaching movements, along with three other benchmark gain sets not optimized for our system, on a large set of dynamic reaching movements for which the controllers had not been optimized, to test ability to generalize. Robustness in the presence of weakened muscles was also tested. The two optimized gain sets were found to have very similar performance to each other on all metrics, and to exhibit significantly better accuracy, compared with the three standard gain sets. All gain sets investigated used physiologically acceptable amounts of muscular activation. It was concluded that optimization can yield significant improvements in controller performance while still maintaining muscular efficiency, and that optimization should be considered as a strategy for future neuroprosthesis controller design.

Real-time simulation of three-dimensional shoulder girdle and arm dynamics.

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

Glenohumeral stability during a hand-positioning task in previously injured shoulders.

This study aimed to compare glenohumeral stability during functional tasks in subjects with previous dislocation injury against non-injured controls. Six subjects with previous injury and six controls were asked to complete hand-positioning tasks against external forces applied in six directions. Arm kinematics, muscle activations and hand forces were measured and used as input to an inverse-dynamic model of the shoulder that optimised muscle forces to solve the load-sharing problem. Glenohumeral stability was calculated using the direction of the joint reaction force vector in the glenoid. The simulations showed that GH stability was significantly lower in the previously injured group compared to the controls, and that the direction of exerted forces had a significant effect on GH stability, with the hand pushing away from the body and medially producing significantly lower stability. GH stability was significantly lower in the previously injured group for all six force directions, even though all participants were back to normal activities and reported no symptoms from their injuries.

Selection of muscle and nerve-cuff electrodes for neuroprostheses using customizable musculoskeletal model.

Neuroprosthetic systems based on functional electrical stimulation aim to restore motor function to individuals with paralysis following spinal cord injury. Identifying the optimal electrode set for the neuroprosthesis is complicated because it depends on the characteristics of the individual (such as injury level), the force capacities of the muscles, the movements the system aims to restore, and the hardware limitations (number and type of electrodes available). An electrode-selection method has been developed that uses a customized musculoskeletal model. Candidate electrode sets are created based on desired functional outcomes and the hard ware limitations of the proposed system. Inverse-dynamic simulations are performed to determine the proportion of target movements that can be accomplished with each set; the set that allows the most movements to be performed is chosen as the optimal set. The technique is demonstrated here for a system recently developed by our research group to restore whole-arm movement to individuals with high-level tetraplegia. The optimal set included selective nerve-cuff electrodes for the radial and musculocutaneous nerves; single-channel cuffs for the axillary, suprascapular, upper subscapular, and long-thoracic nerves; and muscle-based electrodes for the remaining channels. The importance of functional goals, hardware limitations, muscle and nerve anatomy, and surgical feasibility are highlighted.

Implicit methods for efficient musculoskeletal simulation and optimal control.

The ordinary differential equations for musculoskeletal dynamics are often numerically stiff and highly nonlinear. Consequently, simulations require small time steps, and optimal control problems are slow to solve and have poor convergence. In this paper, we present an implicit formulation of musculoskeletal dynamics, which leads to new numerical methods for simulation and optimal control, with the expectation that we can mitigate some of these problems. A first order Rosenbrock method was developed for solving forward dynamic problems using the implicit formulation. It was used to perform real-time dynamic simulation of a complex shoulder arm system with extreme dynamic stiffness. Simulations had an RMS error of only 0.11 degrees in joint angles when running at real-time speed. For optimal control of musculoskeletal systems, a direct collocation method was developed for implicitly formulated models. The method was applied to predict gait with a prosthetic foot and ankle. Solutions were obtained in well under one hour of computation time and demonstrated how patients may adapt their gait to compensate for limitations of a specific prosthetic limb design. The optimal control method was also applied to a state estimation problem in sports biomechanics, where forces during skiing were estimated from noisy and incomplete kinematic data. Using a full musculoskeletal dynamics model for state estimation had the additional advantage that forward dynamic simulations, could be done with the same implicitly formulated model to simulate injuries and perturbation responses. While these methods are powerful and allow solution of previously intractable problems, there are still considerable numerical challenges, especially related to the convergence of gradient-based solvers.

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system.

A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4 degrees in ideal conditions, and less than 10 degrees even in the case of considerable fatigue and external disturbances.

A real-time, 3-D musculoskeletal model for dynamic simulation of arm movements.

Neuroprostheses can be used to restore movement of the upper limb in individuals with high-level spinal cord injury. Development and evaluation of command and control schemes for such devices typically require real-time, "patient-in-the-loop" experimentation. A real-time, 3-D, musculoskeletal model of the upper limb has been developed for use in a simulation environment to allow such testing to be carried out noninvasively. The model provides real-time feedback of human arm dynamics that can be displayed to the user in a virtual reality environment. The model has a 3-DOF glenohumeral joint as well as elbow flexion/extension and pronation/supination and contains 22 muscles of the shoulder and elbow divided into multiple elements. The model is able to run in real time on modest desktop hardware and demonstrates that a large-scale, 3-D model can be made to run in real time. This is a prerequisite for a real-time, whole-arm model that will form part of a dynamic arm simulator for use in the development, testing, and user training of neural prosthesis systems.

Musculoskeletal model-guided, customizable selection of shoulder and elbow muscles for a C5 SCI neuroprosthesis.

Individuals with C5/C6 spinal cord injury (SCI) have a number of paralyzed muscles in their upper extremities that can be electrically activated in a coordinated manner to restore function. The selection of a practical subset of paralyzed muscles for stimulation depends on the specific condition of the individual, the functions targeted for restoration, and surgical considerations. This paper presents a musculoskeletal model-based approach for optimizing the muscle set used for functional electrical stimulation (FES) of the shoulder and elbow in this population. Experimentally recorded kinematics from able-bodied subjects served as inputs to a musculoskeletal model of the shoulder and elbow, which was modified to reflect the reduced muscle force capacities of an individual with C5 SCI but also the potential of using FES to activate paralyzed muscles. A large number of inverse dynamic simulations mimicking typical activities of daily living were performed that included 1) muscles with retained voluntary control and 2) many different combinations of stimulated paralyzed muscles. These results indicate that a muscle set consisting of the serratus anterior, infraspinatus and triceps would enable the greatest range of relevant movements. This set will become the initial target in a C5SCI neuroprosthesis to restore shoulder and elbow function.

A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems.

Upper extremity neuroprostheses use functional electrical stimulation (FES) to restore arm motor function to individuals with cervical level spinal cord injury. For the design and testing of these systems, a biomechanical model of the shoulder and elbow has been developed, to be used as a substitute for the human arm. It can be used to design and evaluate specific implementations of FES systems, as well as FES controllers. The model can be customized to simulate a variety of pathological conditions. For example, by adjusting the maximum force the muscles can produce, the model can be used to simulate an individual with tetraplegia and to explore the effects of FES of different muscle sets. The model comprises six bones, five joints, nine degrees of freedom, and 29 shoulder and arm muscles. It was developed using commercial, graphics-based modeling and simulation packages that are easily accessible to other researchers and can be readily interfaced to other analysis packages. It can be used for both forward-dynamic (inputs: muscle activation and external load; outputs: motions) and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations. Our model was verified by comparing the model calculated muscle activations to electromyographic signals recorded from shoulder and arm muscles of five subjects. As an example of its application to neuroprosthesis design, the model was used to demonstrate the importance of rotator cuff muscle stimulation when aiming to restore humeral elevation. It is concluded that this model is a useful tool in the development and implementation of upper extremity neuroprosthetic systems.

Adaptive neural network controller for an upper extremity neuroprosthesis.

The long term goal of this project is to develop an adaptive neural network controller for an upper extremity neuroprosthesis targeted for people with C5/C6 spinal cord injury (SCI). The challenge is to determine how to simultaneously stimulate different paralyzed muscles based on the EMG activity of muscles under retained voluntary control. The controller extracts the movement intention from the recorded EMG signals and generates the appropriate stimulation levels to activate the paralyzed muscles. To test the feasibility of this controller, different arm movements were recorded from able bodied subjects. Using a musculoskeletal model of the arm, inverse simulations provided muscle activation patterns corresponding to these movements. The model was modified to reflect C5/C6 SCI and the optimization criteria were varied to reflect different nervous system motor control strategies. Activation patterns were then used to train a time-delayed neural network to predict paralyzed muscle activations from voluntary muscle activations. Forward simulations were performed to obtain predicted movements and use the kinematic errors to design an adaptive strategy to account for disturbances and changes in the system.