Improving Ethical Review of Research Involving Incentives for Health Promotion

Alex London and colleagues propose new ethical frameworks for evaluating the risks associated with research in which financial or other incentives are used to promote healthy behavior

Personalised profiling to identify clinically relevant changes in tremor due to multiple sclerosis

Background: There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual's tremor symptoms, due to poor test-retest repeatability. Method: We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual's tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject's self-assessment of the impact of tremor on their activities of daily living (FTMTRS C). Results: Unlike the conventional sensor-based and clinical metrics, the newly proposed ’change in scale’ metrics presented statistically significant correlations with changes in self-assessed impact of tremor (max R2>0.5,p< 0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance (R2=0.25 vs. R2=0.15 for conventional clinical observation, both p< 0.05).Conclusions: The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms

A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Financial Incentive Increases CPAP Acceptance in Patients from Low Socioeconomic Background

OBJECTIVE: We explored whether financial incentives have a role in patients' decisions to accept (purchase) a continuous positive airway pressure (CPAP) device in a healthcare system that requires cost sharing. DESIGN: Longitudinal interventional study. PATIENTS: The group receiving financial incentive (n = 137, 50.8±10.6 years, apnea/hypopnea index (AHI) 38.7±19.9 events/hr) and the control group (n = 121, 50.9±10.3 years, AHI 39.9±22) underwent attendant titration and a two-week adaptation to CPAP. Patients in the control group had a co-payment of $330-660; the financial incentive group paid a subsidized price of$55. RESULTS: CPAP acceptance was 43% greater (p = 0.02) in the financial incentive group. CPAP acceptance among the low socioeconomic strata (n = 113) (adjusting for age, gender, BMI, tobacco smoking) was enhanced by financial incentive (OR, 95% CI) (3.43, 1.09-10.85), age (1.1, 1.03-1.17), AHI (>30 vs. <30) (4.87, 1.56-15.2), and by family/friends who had positive experience with CPAP (4.29, 1.05-17.51). Among average/high-income patients (n = 145) CPAP acceptance was affected by AHI (>30 vs. <30) (3.16, 1.14-8.75), living with a partner (8.82, 1.03-75.8) but not by the financial incentive. At one-year follow-up CPAP adherence was similar in the financial incentive and control groups, 35% and 39%, respectively (p = 0.82). Adherence rate was sensitive to education (+yr) (1.28, 1.06-1.55) and AHI (>30 vs. <30) (5.25, 1.34-18.5). CONCLUSIONS: Minimizing cost sharing reduces a barrier for CPAP acceptance among low socioeconomic status patients. Thus, financial incentive should be applied as a policy to encourage CPAP treatment, especially among low socioeconomic strata patients

Patient adherence to medical treatment: a review of reviews

BACKGROUND: Patients' non-adherence to medical treatment remains a persistent problem. Many interventions to improve patient adherence are unsuccessful and sound theoretical foundations are lacking. Innovations in theory and practice are badly needed. A new and promising way could be to review the existing reviews of adherence to interventions and identify the underlying theories for effective interventions. That is the aim of our study. METHODS: The study is a review of 38 systematic reviews of the effectiveness of adherence interventions published between 1990 and 2005. Electronic literature searches were conducted in Medline, Psychinfo, Embase and the Cochrane Library. Explicit inclusion and exclusion criteria were applied. The scope of the study is patient adherence to medical treatment in the cure and care sector. RESULTS: Significant differences in the effectiveness of adherence interventions were found in 23 of the 38 systematic reviews. Effective interventions were found in each of four theoretical approaches to adherence interventions: technical, behavioural, educational and multi-faceted or complex interventions. Technical solutions, such as a simplification of the regimen, were often found to be effective, although that does not count for every therapeutic regimen.Overall, our results show that, firstly, there are effective adherence interventions without an explicit theoretical explanation of the operating mechanisms, for example technical solutions. Secondly, there are effective adherence interventions, which clearly stem from the behavioural theories, for example incentives and reminders. Thirdly, there are other theoretical models that seem plausible for explaining non-adherence, but not very effective in improving adherence behaviour. Fourthly, effective components within promising theories could not be identified because of the complexity of many adherence interventions and the lack of studies that explicitly compare theoretical components. CONCLUSION: There is a scarcity of comparative studies explicitly contrasting theoretical models or their components. The relative weight of these theories and the effective components in the interventions designed to improve adherence, need to be assessed in future studies. (aut.ref.

Fragile x syndrome and autism: from disease model to therapeutic targets

Autism is an umbrella diagnosis with several different etiologies. Fragile X syndrome (FXS), one of the first identified and leading causes of autism, has been modeled in mice using molecular genetic manipulation. These Fmr1 knockout mice have recently been used to identify a new putative therapeutic target, the metabotropic glutamate receptor 5 (mGluR5), for the treatment of FXS. Moreover, mGluR5 signaling cascades interact with a number of synaptic proteins, many of which have been implicated in autism, raising the possibility that therapeutic targets identified for FXS may have efficacy in treating multiple other causes of autism

Search for Invisible Decays of a Dark Photon Produced in e(+)e(-) Collisions at BABAR

We search for single-photon events in 53 fb^-1 of e+e- collision data collected with the BaBar detector at the PEP-II B-factory. We look for events with a single high-energy photon and a large missing momentum and energy, consistent with production of a spin-1 particle A' through the process e+e->gamma A', A'->invisible. Such particles, referred to as "dark photons", are motivated by theories applying a U(1) gauge symmetry to dark matter. We find no evidence for such processes and set 90% confidence level upper limits on the coupling strength of A' to e+e- in the mass range m_A'<=8 GeV. In particular, our limits exclude the values of the A' coupling suggested by the dark-photon interpretation of the muon (g-2) anomaly, as well as a broad range of parameters for the dark-sector models.Comment: 9 pages, 13 figures; v2 is the version published in Physical Review Letter