IntroductionExisting mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users’ perspective on the device.Methods and analysisThis protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users’ perspective on the deployed technology and relevance of the mobility assessment.Ethics and disseminationThe study has been granted ethics approval by the centre’s committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available.Trial registration numberISRCTN (12246987).

Exercise intolerance and impaired quality of life (QoL) are characteristic of lung transplant candidates and recipients. This review investigated the effects of exercise training on exercise capacity, QoL and clinical outcomes in pre- and post-operative lung transplant patients.A systematic literature search of PubMed, Nursing and Allied Health, Cochrane (CENTRAL), Scopus and CINAHL databases was conducted from inception until February, 2020. The inclusion criteria were assessment of the impact of exercise training before or after lung transplantation on exercise capacity, QoL or clinical outcomes.21 studies met the inclusion criteria, comprising 1488 lung transplant candidates and 1108 recipients. Studies consisted of five RCTs, two quasi-experimental and 14 single-arm cohort or pilot studies. Exercise training improved or at least maintained exercise capacity and QoL before and after lung transplantation. The impact on clinical outcomes was less clear but suggested a survival benefit. The quality of evidence ranged from fair to excellent.Exercise training appears to be beneficial for patients before and after lung transplantation; however, the evidence for direct causation is limited by the lack of controlled trials. Well-designed RCTs are needed, as well as further research into the effect of exercise training on important post-transplant clinical outcomes, such as time to discharge, rejection, infection, survival and re-hospitalisation.

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Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances. Trial registration ISRCTN – 12246987.

Background The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration ISRCTN12051706.

Background Current evidence suggests that interval exercise training (IET) and continuous exercise training (CET) produce comparable benefits in exercise capacity, cardiorespiratory fitness and symptoms in patients with chronic obstructive pulmonary disease (COPD). However, the effects of these modalities have only been reviewed in patients with COPD. This meta-analysis compares the effectiveness of IET versus CET on exercise capacity, cardiorespiratory fitness and exertional symptoms in patients with chronic respiratory diseases (CRDs). Methods: PubMed, CINHAL, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL) and Nursing and Allied health were searched for randomised controlled trials from inception to September 2020. Eligible studies included the comparison between IET and CET, reporting measures of exercise capacity, cardiorespiratory fitness and symptoms in individuals with CRDs. Results: Thirteen randomised control trials (530 patients with CRDs) with fair to good quality on the PEDro scale were included. Eleven studies involved n = 446 patients with COPD, one involved n = 24 patients with cystic fibrosis (CF) and one n = 60 lung transplantation (LT) candidates. IET resulted in greater improvements in peak work rate (WRpeak) (2.40 W, 95% CI: 0.83 to 3.97 W; p = 0.003) and lower exercise-induced dyspnoea (−0.47, 95% CI: −0.86 to 0.09; p = 0.02) compared to CET; however, these improvements did not exceed the minimal important difference for these outcomes. No significant differences in peak values for oxygen uptake (VO2peak), heart rate (HRpeak), minute ventilation (VEpeak), lactate threshold (LAT) and leg discomfort were found between the interventions. Conclusions: IET is superior to CET in improving exercise capacity and exercise-induced dyspnoea sensations in patients with CRDs; however, the extent of the clinical benefit is not considered clinically meaningful.

Highlights• The study provides proof of concept on how to select COPD patients likely to respond to portable NIV (pNIV) during intermittent exercise.• One third of patients (8/24) did not improve dynamic hyperinflation (DH nonresponders) with the application of pNIV compared to pursed lip breathing (PLB).• DH non-responders exhibited greater resting hyperinflation and tend towards worse spirometric measures compared to responders.