Carmelo Velardo

A Survey of Mobile Phone Sensing, Self-reporting and Social Sharing for Pervasive Healthcare

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individuals community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis

Background Diabetes in pregnancy is a global problem. Technological innovations present exciting opportunities for novel approaches to improve clinical care delivery for gestational and other forms of diabetes in pregnancy. Objective To perform an updated and comprehensive systematic review and meta-analysis of the literature to determine whether telemedicine solutions offer any advantages compared with the standard care for women with diabetes in pregnancy. Methods The review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Randomized controlled trials (RCT) in women with diabetes in pregnancy that compared telemedicine blood glucose monitoring with the standard care were identified. Searches were performed in SCOPUS and PubMed, limited to English language publications between January 2000 and January 2016. Trials that met the eligibility criteria were scored for risk of bias using the Cochrane Collaborations Risk of Bias Tool. A meta-analysis was performed using Review Manager software version 5.3 (Nordic Cochrane Centre, Cochrane Collaboration). Results A total of 7 trials were identified. Meta-analysis demonstrated a modest but statistically significant improvement in HbA1c associated with the use of a telemedicine technology. The mean HbA1c of women using telemedicine was 5.33% (SD 0.70) compared with 5.45% (SD 0.58) in the standard care group, representing a mean difference of −0.12% (95% CI −0.23% to −0.02%). When this comparison was limited to women with gestational diabetes mellitus (GDM) only, the mean HbA1c of women using telemedicine was 5.22% (SD 0.70) compared with 5.37% (SD 0.61) in the standard care group, mean difference −0.14% (95% CI −0.25% to −0.04%). There were no differences in other maternal and neonatal outcomes reported. Conclusions There is currently insufficient evidence that telemedicine technology is superior to standard care for women with diabetes in pregnancy; however, there was no evidence of harm. No trials were identified that assessed patient satisfaction or cost of care delivery, and it may be in these areas where these technologies may be found most valuable.

Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

Background Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD. Objective The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter. Methods We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine–based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations. Results On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity. Conclusions All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6olpMWNpc)

Trial protocol to compare the efficacy of a smartphone-based blood glucose management system with standard clinic care in the gestational diabetic population

Introduction The prevalence of gestational diabetes mellitus (GDM) is rising in the UK. Good glycaemic control improves maternal and neonatal outcomes. Frequent clinical review of patients with GDM by healthcare professionals is required owing to the rapidly changing physiology of pregnancy and its unpredictable course. Novel technologies that allow home blood glucose (BG) monitoring with results transmitted in real time to a healthcare professional have the potential to deliver good-quality healthcare to women more conveniently and at a lower cost to the patient and the healthcare provider compared to the conventional face-to-face or telephone-based consultation. We have developed an integrated GDm-health management system and aim to test the impact of using this system on maternal glycaemic control, costs, patient satisfaction and maternal and neonatal outcomes compared to standard clinic care in a single large publicly funded (National Health Service (NHS)) maternity unit. Methods and analysis Women with confirmed gestational diabetes in a current pregnancy are individually randomised to either the GDm-health system and half the normal clinic visits or normal clinic care. Primary outcome is mean BG in each group from recruitment to delivery calculated, with adjustments made for number of BG measurements, proportion of preprandial and postprandial readings and length of time in study, and compared between the groups. The secondary objective will be to compare the two groups for compliance to the allocated BG monitoring regime, maternal and neonatal outcomes, glycaemic control using glycated haemoglobin (HbA1c) and other BG metrics, and patient attitudes to care assessed using a questionnaire and resource use. Ethics and dissemination Thresholds for treatment, dietary advice and clinical management are the same in both groups. The results of the study will be published in a peer-reviewed journal and disseminated electronically and in print. Trial registration number NCT01916694; Pre-results.

Self-Management Support Using a Digital Health System Compared With Usual Care for Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial

Background We conducted a randomized controlled trial of a digital health system supporting clinical care through monitoring and self-management support in community-based patients with moderate to very severe chronic obstructive pulmonary disease (COPD). Objective The aim of this study was to determine the efficacy of a fully automated Internet-linked, tablet computer-based system of monitoring and self-management support (EDGE‚ sElf-management anD support proGrammE) in improving quality of life and clinical outcomes. Methods We compared daily use of EDGE with usual care for 12 months. The primary outcome was COPD-specific health status measured with the St George’s Respiratory Questionnaire for COPD (SGRQ-C). Results A total of 166 patients were randomized (110 EDGE, 56 usual care). All patients were included in an intention to treat analysis. The estimated difference in SGRQ-C at 12 months (EDGE−usual care) was −1.7 with a 95% CI of −6.6 to 3.2 (P=.49). The relative risk of hospital admission for EDGE was 0.83 (0.56-1.24, P=.37) compared with usual care. Generic health status (EQ-5D, EuroQol 5-Dimension Questionnaire) between the groups differed significantly with better health status for the EDGE group (0.076, 95% CI 0.008-0.14, P=.03). The median number of visits to general practitioners for EDGE versus usual care were 4 versus 5.5 (P=.06) and to practice nurses were 1.5 versus 2.5 (P=.03), respectively. Conclusions The EDGE clinical trial does not provide evidence for an effect on COPD-specific health status in comparison with usual care, despite uptake of the intervention. However, there appears to be an overall benefit in generic health status; and the effect sizes for improved depression score, reductions in hospital admissions, and general practice visits warrants further evaluation and could make an important contribution to supporting people with COPD. Trial registration International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6pmfIJ9KK)

Personalized alerts for patients with COPD using pulse oximetry and symptom scores

Chronic Obstructive Pulmonary Disease (COPD) is a progressive chronic disease, predicted to become the third leading cause of death by 2030. COPD patients are at risk of sudden and acute worsening of symptoms, reducing the patients quality of life and leading to hospitalization. We present the results of a pilot study with 18 COPD patients using an m-Health system, based on a tablet computer and pulse oximeter, for a period of six months. For prioritizing patients for clinical review, a data-driven approach has been developed which generates personalized alerts using the electronic symptom diary, pulse rate, blood oxygen saturation, and respiratory rate derived from oximetry data. This work examines the advantages of multivariate novelty detection over univariate approaches and shows the benefit of including respiratory rate as a predictor.

Digital blood glucose monitoring could provide new objective assessments of blood glucose control in women with gestational diabetes

Assessing blood glucose (BG) control in women with gestational diabetes mellitus is challenging, as routine tests, for example HbA1c assessment, are an insensitive measure of response to the progressive changes of glucose regulation in pregnancy [1,2]. Consequently, the standard of care remains visual inspection of BG paper diaries of self-performed capillary monitoring. Numerous telehealth solutions to record BG readings have been developed, but their clinical superiority over standard care is yet to be shown [3]. Whilst several of the studies on telehealth solutions assess digital systems, decision-making still relies on visual inspection of BG trends. We have developed a digital BG management system, GDm-health, in which BG readings are automatically transferred via Bluetooth to a smartphone app, which also allows women to add a meal label and comments (e.g. medications and diet). These annotated readings are then transmitted by the smartphone via the 3G network to a secure website. Feedback messages can be sent to the woman through the website by clinicians reviewing her data, or by telephone call as required. Full details describing the system development and user satisfaction are available [4,5]. We hypothesized that the annotated BG data could provide novel measures of glycaemic control predictive of pregnancy outcomes. We therefore assessed the ability of selected measures to predict the most common pregnancy outcome associated with gestational diabetes, large-forgestational-age (LGA) babies [6]. A 49-patient service development project was carried out in a tertiary centre in the UK (June 2012 to August 2013). Gestational diabetes screening, diagnosis and management was in keeping with NICE 2008 [7] and local guidelines. GDm-health was used for all self-monitoring. Women performed fasting, preand 2-h postprandial capillary testing. Targets were to maintain all readings between 4.0 and 6.0 mmol/l. If readings were within target, monitoring was reduced from 7 to 3 days/week. Birth weight was classified as LGA or non-LGA (NGA). LGA was defined as birth weight ≥90 centile for gestational age and gender [8]. Anonymized baseline characteristics and annotated BG data were extracted from the website after delivery. This project was conducted as a service improvement project. Participation was voluntary and not remunerated. All pregnant women in the Oxford University Hospitals NHS Trust provide consent at the start of pregnancy for anonymized clinical data to be used for research. Maternal characteristics were compared using chi-squared and Mann–Whitney U-tests. Differences between mean, fasting and 2-h postprandial monitoring between NGA and LGA were compared using Student’s t-tests. Two-weekly moving mean BG values with standard errors (SEM) were calculated and presented graphically from 30 weeks until delivery. Odds ratios (ORs) with 95% CIs for LGA babies were calculated using logistic regression, adjusting for maternal factors where P < 0.05. All analyses were performed using SPSS version 21.0 (IBM) and MATLAB version r2014b (Mathworks Inc.). Outcome data were available for 41 women, of whom 12 (29%) delivered LGA babies. These women transmitted 14,222 BG readings, 98.6% of which were annotated. A

A user-centred home monitoring and self-management system for patients with heart failure: a multicentre cohort study

Aims Previous generations of home monitoring systems have had limited usability. We aimed to develop and evaluate a user-centred and adaptive system for health monitoring and self-management support in patients with heart failure. Methods and results Patients with heart failure were recruited from three UK centres and provided with Internet-enabled tablet computers that were wirelessly linked with sensor devices for blood pressure, heart rate, and weight monitoring. Patient observations, interviews, and concurrent analyses of the automatically collected data from their monitoring devices were used to increase the usability of the system. Of the 52 participants (median age 77 years, median follow-up 6 months [interquartile range, IQR, 3.6-9.2]), 24 (46%) had no, or very limited prior, experience with digital technologies. It took participants about 1.5 min to complete the daily monitoring tasks, and the rate of failed attempts in completing tasks was <5%. After 45 weeks of observation, participants still used the system on 4.5 days per week (confidence interval 3.2-5.7 days). Of the 46 patients who could complete the final survey, 93% considered the monitoring system as easy to use and 38% asked to keep the system for self-management support after the study was completed. Conclusion We developed a user-centred home monitoring system that enabled a wide range of heart failure patients, with differing degrees of IT literacy, to monitor their health status regularly. Despite no active medical intervention, patients felt that they benefited from the reassurance and sense of connectivity that the monitoring system provided.

Supporting heart failure patients through personalized mobile health monitoring

Heart failure is a common chronic condition requiring frequent attention and ongoing provision of healthcare services. In this context we present a personalized mobile-based home monitoring system aiming to support heart failure patients in daily self-monitoring of their condition. An Internet-linked tablet computer and various portable and wearable sensing devices are employed in order to monitor the patients physiological parameters and enable healthcare professionals to review patients status remotely. The proposed system supports the activation/deactivation of system functional components by healthcare professionals during run-time operation, the unobtrusive remote upgrade of the mobile system through a private application distribution channel, and the automatic recording of user interactions, in order to meet the patients ongoing individualized preferences and healthcare needs. Preliminary results from an observational cohort study indicate that heart failure patients find the proposed system acceptable and consider it useful for self-monitoring their condition.

Nudging people with Type 2 diabetes towards better self-management through personalized risk communication: A pilot randomized controlled trial in primary care

To assess the feasibility in routine primary care consultation and investigate the effect on risk recall and self‐management of a new type of risk communication intervention based on behavioural economics (“nudge‐based”) for people with Type 2 diabetes mellitus (T2DM).