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BackgroundTreatment of hyperglycemia in women with gestational diabetes mellitus (GDM) is associated with improved maternal and neonatal outcomes and requires intensive clinical input. This is currently achieved by hospital clinic attendance every 2 to 4 weeks with limited opportunity for intervention between these visits.ObjectiveWe conducted a randomized controlled trial to determine whether the use of a mobile phone-based real-time blood glucose management system to manage women with GDM remotely was as effective in controlling blood glucose as standard care through clinic attendance.MethodsWomen with an abnormal oral glucose tolerance test before 34 completed weeks of gestation were individually randomized to a mobile phone-based blood glucose management solution (GDm-health, the intervention) or routine clinic care. The primary outcome was change in mean blood glucose in each group from recruitment to delivery, calculated with adjustments made for number of blood glucose measurements, proportion of preprandial and postprandial readings, baseline characteristics, and length of time in the study.ResultsA total of 203 women were randomized. Blood glucose data were available for 98 intervention and 85 control women. There was no significant difference in rate of change of blood glucose (–0.16 mmol/L in the intervention and –0.14 mmol/L in the control group per 28 days, P=.78). Women using the intervention had higher satisfaction with care (P=.049). Preterm birth was less common in the intervention group (5/101, 5.0% vs 13/102, 12.7%; OR 0.36, 95% CI 0.12-1.01). There were fewer cesarean deliveries compared with vaginal deliveries in the intervention group (27/101, 26.7% vs 47/102, 46.1%, P=.005). Other glycemic, maternal, and neonatal outcomes were similar in both groups. The median time from recruitment to delivery was similar (intervention: 54 days; control: 49 days; P=.23). However, there were significantly more blood glucose readings in the intervention group (mean 3.80 [SD 1.80] and mean 2.63 [SD 1.71] readings per day in the intervention and control groups, respectively; P<.001). There was no significant difference in direct health care costs between the two groups, with a mean cost difference of the intervention group compared to control of –£1044 (95% CI –£2186 to £99). There were no unexpected adverse outcomes.ConclusionsRemote blood glucocse monitoring in women with GDM is safe. We demonstrated superior data capture using GDm-health. Although glycemic control and maternal and neonatal outcomes were similar, women preferred this model of care. Further studies are required to explore whether digital health solutions can promote desired self-management lifestyle behaviors and dietetic adherence, and influence maternal and neonatal outcomes. Digital blood glucose monitoring may provide a scalable, practical method to address the growing burden of GDM around the world.Trial RegistrationClinicalTrials.gov NCT01916694; https://clinicaltrials.gov/ct2/show/NCT01916694 (Archived by WebCite at http://www.webcitat...

SummaryBackgroundSleep disturbance occurs in most patients with delusions or hallucinations and should be treated as a clinical problem in its own right. However, cognitive behavioural therapy (CBT)—the best evidence-based treatment for insomnia—has not been tested in this patient population. We aimed to pilot procedures for a randomised trial testing CBT for sleep problems in patients with current psychotic experiences, and to provide a preliminary assessment of potential benefit.MethodsWe did this prospective, assessor-blind, randomised controlled pilot trial (Better Sleep Trial [BEST]) at two mental health centres in the UK. Patients (aged 18–65 years) with persistent distressing delusions or hallucinations in the context of insomnia and a schizophrenia spectrum diagnosis were randomly assigned (1:1), via a web-based randomisation system with minimisation to balance for sex, insomnia severity, and psychotic experiences, to receive either eight sessions of CBT plus standard care (medication and contact with the local clinical team) or standard care alone. Research assessors were masked to group allocation. Assessment of outcome was done at weeks 0, 12 (post-treatment), and 24 (follow-up). The primary efficacy outcomes were insomnia assessed by the Insomnia Severity Index (ISI) and delusions and hallucinations assessed by the Psychotic Symptoms Rating Scale (PSYRATS) at week 12. We did analysis by intention to treat, with an aim to provide confidence interval estimation of treatment effects. This study is registered with ISRCTN, number 33695128.FindingsBetween Dec 14, 2012, and May 22, 2013, and Nov 7, 2013, and Aug 26, 2014, we randomly assigned 50 patients to receive CBT plus standard care (n=24) or standard care alone (n=26). The last assessments were completed on Feb 10, 2015. 48 (96%) patients provided follow-up data. 23 (96%) patients offered CBT took up the intervention. Compared with standard care, CBT led to reductions in insomnia in the large effect size range at week 12 (adjusted mean difference 6·1, 95% CI 3·0–9·2, effect size d=1·9). By week 12, nine (41%) of 22 patients receiving CBT and one (4%) of 25 patients receiving standard care alone no longer had insomnia, with ISI scores lower than the cutoff for insomnia. The treatment effect estimation for CBT covered a range from reducing but also increasing delusions (adjusted mean difference 0·3, 95% CI −2·0 to 2·6) and hallucinations (−1·9, −6·5 to 2·7). Three patients, all in the CBT group, had five adverse events, although none were regarded as related to study treatment.InterpretationOur findings show that CBT for insomnia might be highly effective for improving sleep in patients with persistent delusions or hallucinations. A larger, suitably powered phase 3 study is now needed to provide a precise estimate of the effects of CBT for sleep problems, both on sleep and psychotic experiences.FundingResearch for Patient Benefit Programme, National Institute for Health Research.

BackgroundT-cell responses against highly conserved influenza antigens have been previously associated with protection. However, these immune responses are poorly maintained following recovery from influenza infection and are not boosted by inactivated influenza vaccines. We have previously demonstrated the safety and immunogenicity of two viral vectored vaccines, modified vaccinia virus Ankara (MVA) and the chimpanzee adenovirus ChAdOx1 expressing conserved influenza virus antigens, nucleoprotein (NP) and matrix protein-1 (M1). We now report on the safety and long-term immunogenicity of multiple combination regimes of these vaccines in young and older adults.MethodsWe conducted a Phase I open-label, randomized, multi-center study in 49 subjects aged 18–46 years and 24 subjects aged 50 years or over. Following vaccination, adverse events were recorded and the kinetics of the T cell response determined at multiple time points for up to 18 months.FindingsBoth vaccines were well tolerated. A two dose heterologous vaccination regimen significantly increased the magnitude of pre-existing T-cell responses to NP and M1 after both doses in young and older adults. The fold-increase and peak immune responses after a single MVA-NP + M1 vaccination was significantly higher compared to ChAdOx1 NP + M1. In a mixed regression model, T-cell responses over 18 months were significantly higher following the two dose vaccination regimen of MVA/ChAdOx1 NP + M1.InterpretationA two dose heterologous vaccination regimen of MVA/ChAdOx1 NP + M1 was safe and immunogenic in young and older adults, offering a promising vaccination strategy for inducing long-term broadly cross-reactive protection against influenza A.Funding SourceMedical Research Council UK, NIHR BMRC Oxford.

Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art.

The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.

BackgroundWhen using a continuous outcome measure in a randomised controlled trial (RCT), the baseline score should be measured in addition to the post-intervention score, and it should be analysed using the appropriate statistical analysis.MethodsWe derive the correlation between the change score and baseline score and show that there is always a correlation (usually negative) between the change score and baseline score. We discuss the following correlations and provide the mathematical derivations in the Appendix:Correlation between change score and baseline scoreCorrelation between change score and post scoreCorrelation between change score and average score.The setting here is a parallel, two-arm RCT, but the method discussed in this paper is applicable for any studies or trials that have a continuous outcome measure; it is not restricted to RCTs.ResultsWe show that using the change score as the outcome measure does not address the problem of regression to the mean, nor does it take account of the baseline imbalance. Whether the outcome is change score or post score, one should always adjust for baseline using analysis of covariance (ANCOVA); otherwise, the estimated treat effect may be biased. We show that these correlations also apply when comparing two measurement methods using Bland-Altman plots.ConclusionsThe correlation between baseline and post-intervention scores can be derived using the variance sum law. We can then use the derived correlation to calculate the required sample size in the design stage. Baseline imbalance may occur in RCTs, and ANCOVA should be used to adjust for baseline in the analysis stage.