Recruiting patients to a digital self-management study whilst in hospital for a chronic obstructive pulmonary disease exacerbation: A feasibility analysis

Background Patients with chronic obstructive pulmonary disease (COPD) are often hospitalised with acute exacerbations (AECOPD) and many patients get readmitted. Intervening with hospitalised patients may be optimal timing to provide support. Our previous work demonstrated use of a digital monitoring and self-management support tool in the community. However, we wanted to explore the feasibility of recruiting patients whilst hospitalised for an AECOPD, and to identify the rate of dropout attrition around admission for AECOPD. Methods Patients were recruited to the EDGE2 study between May 2019 and March 2020. Patients were identified by the clinical teams and patients were recruited by members of the clinical research team. Participants were aged 40 years or older, had a diagnosis of COPD and were attending or admitted to hospital for an AECOPD. Participants were given a tablet computer, Bluetooth-linked pulse oximeter and wrist-worn physical activity monitor to use until 6 months post-discharge. Use of the system aimed to support COPD self-management by enabling self-monitoring of vital signs, COPD symptoms, mood and physical activity, and access to multi-media educational resources. Results 281 patients were identified and 126 approached. The main referral source was the specialist respiratory nursing and physiotherapist team (49.8% of patients identified). Twenty-six (37.1%) patients were recruited. As of 21 April 2020, 14 (53.8%) participants withdrew and 11 (of 14; 78.6%) participants withdrew within four weeks of discharge. The remaining participants withdrew between one and three months follow-up (1 of 14; 7.1%) and between three and six months follow-up (2 of 14; 14.3%). Conclusion A large number of patients were screened to recruit a relatively small sample and a high rate of dropout was observed. It does not appear feasible to recruit patients with COPD to digital interventional studies from the hospital setting when they have the burden of coping with acute illness

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.</p

Supporting People With Type 2 Diabetes in the Effective Use of Their Medicine Through Mobile Health Technology Integrated With Clinical Care to Reduce Cardiovascular Risk : Protocol for an Effectiveness and Cost-effectiveness Randomized Controlled Trial

Funding Information: The Support Through Mobile Messaging and Digital Health Technology for Diabetes research team acknowledges the support of the National Institute for Health Research (NIHR) through the Clinical Research Networks. AF, LT, and RR have received support from the NIHR Oxford Biomedical Research Centre. RH received support from the NIHR Collaboration for Leadership in Applied Health Research and Care and North Thames at Bart's Health National Health Service (NHS) Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. This paper presents independent research funded by the NIHR under its Program Grants for Applied Research as part of a wider program of work (RP-PG-1214-20003). The authors thank the personnel of the University of Oxford Primary Care and Vaccines Clinical Trials Collaborative for providing support in the conduct of the trial.Peer reviewedPublisher PD

Physical activity practiced at a young age is associated with a less severe subsequent clinical presentation in facioscapulohumeral muscular dystrophy

Background: In facioscapulohumeral muscular dystrophy (FSHD), it is not known whether physical activity (PA) practiced at young age is associated with the clinical presentation of disease. To assess this issue, we performed a retrospective cohort study concerning the previous practice of sports and, among them, those with medium-high cardiovascular commitment in clinically categorized carriers of a D4Z4 reduced allele (DRA). Methods: People aged between 18 and 60 were recruited as being DRA carriers. Subcategory (classical phenotype, A; incomplete phenotype, B; asymptomatic carriers, C; complex phenotype, D) and FSHD score, which measures muscle functional impairment, were assessed for all participants. Information on PAs was retrieved by using an online survey dealing with the practice of sports at a young age. Results: 368 participants were included in the study, average age 36.6 years (SD = 9.4), 47.6% male. The FSHD subcategory A was observed in 157 (42.7%) participants with average (± SD) FSHD score of 5.8 ± 3.0; the incomplete phenotype (category B) in 46 (12.5%) participants (average score 2.2 ± 1.7) and the D phenotype in 61 (16.6%, average score 6.5 ± 3.8). Asymptomatic carriers were 104 (subcategory C, 28.3%, score 0.0 ± 0.2). Time from symptoms onset was higher for patients with A (15.8 ± 11.1 years) and D phenotype (13.3 ± 11.9) than for patients with B phenotype (7.3 ± 9.0). The practice of sports was associated with lower FSHD score (-17%) in participants with A phenotype (MR = 0.83, 95% CI = 0.73-0.95, p = 0.007) and by 33% in participants with D phenotype (MR = 0.67, 95% CI = 0.51-0.89, p = 0.006). Conversely, no improvement was observed in participants with incomplete phenotype with mild severity (B). Conclusions: PAs at a young age are associated with a lower clinical score in the adult A and D FSHD subcategories. These results corroborate the need to consider PAs at the young age as a fundamental indicator for the correct clinical stratification of the disease and its possible evolution

Blood pressure monitoring in high-risk pregnancy to improve the detection and monitoring of hypertension (the BUMP 1 and 2 trials): protocol for two linked randomised controlled trials.

INTRODUCTION: Self-monitoring of blood pressure (BP) in pregnancy could improve the detection and management of pregnancy hypertension, while also empowering and engaging women in their own care. Two linked trials aim to evaluate whether BP self-monitoring in pregnancy improves the detection of raised BP during higher risk pregnancies (BUMP 1) and whether self-monitoring reduces systolic BP during hypertensive pregnancy (BUMP 2). METHODS AND ANALYSES: Both are multicentre, non-masked, parallel group, randomised controlled trials. Participants will be randomised to self-monitoring with telemonitoring or usual care. BUMP 1 will recruit a minimum of 2262 pregnant women at higher risk of pregnancy hypertension and BUMP 2 will recruit a minimum of 512 pregnant women with either gestational or chronic hypertension. The BUMP 1 primary outcome is the time to the first recording of raised BP by a healthcare professional. The BUMP 2 primary outcome is mean systolic BP between baseline and delivery recorded by healthcare professionals. Other outcomes will include maternal and perinatal outcomes, quality of life and adverse events. An economic evaluation of BP self-monitoring in addition to usual care compared with usual care alone will be assessed across both study populations within trial and with modelling to estimate long-term cost-effectiveness. A linked process evaluation will combine quantitative and qualitative data to examine how BP self-monitoring in pregnancy is implemented and accepted in both daily life and routine clinical practice. ETHICS AND DISSEMINATION: The trials have been approved by a Research Ethics Committee (17/WM/0241) and relevant research authorities. They will be published in peer-reviewed journals and presented at national and international conferences. If shown to be effective, BP self-monitoring would be applicable to a large population of pregnant women. TRIAL REGISTRATION NUMBER: NCT03334149.This work is funded from a National Institute for Health Research (NIHR) Programme grant for applied research (RP-PG- 1209-10051) and an NIHR Professorship awarded to RJM (NIHR-RP- R2- 12-015). RJM and KLT receive funding from the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Oxford at Oxford Health NHS Foundation Trust. JS is a National Institute for Health Research (NIHR) Senior Investigator and supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London (NIHR CLAHRC South London) at King’s College Hospital NHS Foundation Trust. Service support costs will be administered through the NIHR Clinical Research Network

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)

Anthropometry and soft biometrics for smart monitoring

Les récents progrès dans le domaine des communications homme-machine ont créé un intérêt croissant pour l’analyse sémantique des contenus multimédia. Cependant, il reste encore beaucoup à faire pour les machines puissent interpréter le contenu d’une manière plus sémantique/humaine. Aussi en ce qui concerne les systèmes biométriques, des avancées ont été réalisées avec l’introduction duconcept de biométries douces (BD). La BD est un nouveau domaine qui vise à explorer les traits de l’homme, toute en permettantl’intercommunication entre l’homme et la machine. Ici, nous explorons ce concept par rapport à la structure du corps humain, tant dans un cadre théorique, que dans un contexte pratique. Grâce aux bases de données utilisées pour des études démographiques, nouseffectuons une analyse des différentes parties du corps pour extraire de l’information comme le poids, la taille et le genre de la personne. Nous utilisons les mesures anthropométriques pour une opération d’élagage permettant une exécution plus rapide d’un algorithme de reconnaissance biométrique. Ensuite, dans le cas d’un groupe limité d’utilisateurs, la BD est utilisée pour ré-identifier les utilisateurs d’un système de vidéo surveillance. Enfin, nous utilisons l’analyse de flux vidéo 3D pour deux applications lié au domaine para-médical. La première démontre la faisabilité d’analyser la santé apparente (du corps) de l’utilisateur et de donner des conseils basés sur des connaissances médicales. La seconde application permet de contrôler visuellement une éventuelle porte de poids des cosmonautes en absence de gravité.The increasingly rapid convergence between machine and human languages has created a growing interest In the semintic analysis ofmultimedia contents. Despite the recent technological progresses, much remains to study in order to fill the semantic gap, the lack ofcoincidence between the information the machine extracts from raw data and the way humans interpret that content. Soft Biometrics (SB)represent the recent advances that Biometric research performed in these regards.Those traits have attracted the attention of theresearch community in that they have some characteristics peculiar to Hard Biometrics while they improve the semantic content theycarry. In this dissertation we explore the Body SB concept both from a theoretical and a practical point of view. Taking advantage ofdataset used for medicals and demographics studies, we analyze the relation between body parts and personal traits like anthropometricmeasures, weight, and gender. The ability to extract this information is tested by using both the 2D image analysis and by processing 3D video streams. We use SB for pruning a biometric database, enabling faster and more accurate response of a face recognition system.Moreover, we build an application that demonstrates the feasibility of people re-identification for limited groups of users. Furthermore, we present two medical applications for health conditions tracking. The former enables the interaction between the user and an automaticsystem that performs a medical check up of user’s body, providing hints and lifestyle suggestions. The latter Is intended to support themonitoring of cosmonauts’ weight losses due to the lack of gravity in outer space