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Abstract This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987.

BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Abstract 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

Summary: Background: We aimed to assess whether exposure to risk factors in early life from conception to puberty continue to contribute to lung function decline later in life by using a pooled cohort comprising approx. 11,000 adults followed for more than 20 years and with up to three lung function measurements. Methods: Participants (20–68 years) in the ECRHS and NFBC1966 cohort studies followed in the periods 1991–2013 and 1997–2013, respectively, were included. Mean annual decline in maximum forced expired volume in 1 s (FEV1) and forced vital capacity (FVC) were main outcomes. Associations between early life risk factors and change in lung function were estimated using mixed effects linear models adjusted for sex, age, FEV1, FVC and height at baseline, accounting for personal smoking. Findings: Decline in lung function was accelerated in participants with mothers that smoked during pregnancy (FEV1 2.3 ml/year; 95% CI: 0.7, 3.8) (FVC 2.2 ml/year; 0.2, 4.2), with asthmatic mothers (FEV1 2.6 ml/year; 0.9, 4.4) (FEV1/FVC 0.04 per year; 0.04, 0.7) and asthmatic fathers (FVC 2.7 ml/year; 0.5, 5.0), and in women with early menarche (FVC 2.4 ml/year; 0.4, 4.4). Personal smoking of 10 pack-years contributed to a decline of 2.1 ml/year for FEV1 (1.8, 2.4) and 1.7 ml/year for FVC (1.3, 2.1). Severe respiratory infections in early childhood were associated with accelerated decline among ever-smokers. No effect-modification by personal smoking, asthma symptoms, sex or cohort was found. Interpretation: Mothers’ smoking during pregnancy, parental asthma and early menarche may contribute to a decline of FEV1 and FVC later in life comparable to smoking 10 pack-years. Funding: European Union's Horizon 2020; Research Council of Norway; Academy of Finland; University Hospital Oulu; European Regional Development Fund; Spanish Ministry of Science and Innovation; Generalitat de Catalunya.

IntroductionThe clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.MethodsHere, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.Results and discussionThe results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and

Abstract Background Transient receptor potential (TRP) vanilloid and ankyrin cation channels are activated by various noxious chemicals and may play an important role in the pathogenesis of cough. The aim was to study the influence of single nucleotide polymorphisms (SNPs) in TRP genes and irritant exposures on cough. Methods Nocturnal, usual, and chronic cough, smoking, and job history were obtained by questionnaire in 844 asthmatic and 2046 non-asthmatic adults from the Epidemiological study on the Genetics and Environment of Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Occupational exposures to vapors, gases, dusts, and/or fumes were assessed by a job-exposure matrix. Fifty-eight tagging SNPs in TRPV1, TRPV4, and TRPA1 were tested under an additive model. Results Statistically significant associations of 6 TRPV1 SNPs with cough symptoms were found in non-asthmatics after correction for multiple comparisons. Results were consistent across the eight countries examined. Haplotype-based association analysis confirmed the single SNP analyses for nocturnal cough (7-SNP haplotype: p-global = 4.8 × 10-6) and usual cough (9-SNP haplotype: p-global = 4.5 × 10-6). Cough symptoms were associated with exposure to irritants such as cigarette smoke and occupational exposures (p < 0.05). Four polymorphisms in TRPV1 further increased the risk of cough symptoms from irritant exposures in asthmatics and non-asthmatics (interaction p < 0.05). Conclusions TRPV1 SNPs were associated with cough among subjects without asthma from two independent studies in eight European countries. TRPV1 SNPs may enhance susceptibility to cough in current smokers and in subjects with a history of workplace exposures.

Abstract Background Increased use of prostate specific antigen (PSA) has been associated with increased prostate cancer incidence. Ireland is estimated to have one of the highest prostate cancer incidences in Europe and has no national guidelines for prostate cancer screening. GPs have a pivotal role in influencing PSA testing, therefore, our aim was to describe GP testing practices and to identify factors influencing these. Methods A postal survey, including questions on clinical practice and experience, knowledge and demographics was distributed to all GPs (n = 3,683). The main outcomes were (i) PSA testing asymptomatic men and (ii) "inappropriate" PSA testing, defined as testing asymptomatic men aged < 50 or > 75 years. Factors associated with these outcomes were identified using logistic regression. Results 1,625 GPs responded (response rate corrected for eligibility = 53%). Most respondents (79%) would PSA test asymptomatic men. Of these, 34% and 51% would test asymptomatic men < 50 and > 75 years, respectively. In multivariate analyses, GPs were more likely to test asymptomatic men if they were ≥ 50 years, in practice ≥ 10 years, female or less knowledgeable about PSA efficacy. Male GPs who would have a PSA test themselves were > 8-times more likely to PSA test asymptomatic men than GPs who would not have a test. GPs who had an asymptomatic patient diagnosed with prostate cancer following PSA testing, were > 3-times more likely to test asymptomatic men. Practice-related factors positively associated with testing included: running 'well man' clinics, performing occupational health checks and performing other tests routinely with PSA. Factors positively associated with 'inappropriate' testing included; being male and willing to have a PSA test, having worked/trained in the UK and supporting annual PSA testing. 91% of respondents supported the development of national PSA testing guidelines. Conclusion Our findings suggest that widespread PSA testing of asymptomatic men in primary care is primarily due to a combination of clinical experience, poor knowledge and the support of doctors for PSA testing, as evidenced by the willingness of male doctors to have a PSA test. There is an urgent need for education and support for GPs concerning prostate cancer screening, starting with the implementation of national guidelines.

Abstract Background Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN—12246987.

Introduction: Accurately assessing people’s gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors).Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity.Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72–4.87 steps/min, stride length 0.04–0.06 m, walking speed 0.03–0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Abstract The effectiveness of noninvasive respiratory support in severe COVID-19 patients is still controversial. We aimed to compare the outcome of patients with COVID-19 pneumonia and hypoxemic respiratory failure treated with high-flow oxygen administered via nasal cannula (HFNC), continuous positive airway pressure (CPAP) or noninvasive ventilation (NIV), initiated outside the intensive care unit (ICU) in 10 university hospitals in Catalonia, Spain. We recruited 367 consecutive patients aged ≥ 18 years who were treated with HFNC (155, 42.2%), CPAP (133, 36.2%) or NIV (79, 21.5%). The main outcome was intubation or death at 28 days after respiratory support initiation. After adjusting for relevant covariates and taking patients treated with HFNC as reference, treatment with NIV showed a higher risk of intubation or death (hazard ratio 2.01; 95% confidence interval 1.32–3.08), while treatment with CPAP did not show differences (0.97; 0.63–1.50). In the context of the pandemic and outside the intensive care unit setting, noninvasive ventilation for the treatment of moderate to severe hypoxemic acute respiratory failure secondary to COVID-19 resulted in higher mortality or intubation rate at 28 days than high-flow oxygen or CPAP. This finding may help physicians to choose the best noninvasive respiratory support treatment in these patients. Clinicaltrials.gov identifier: NCT04668196.

Rationale and objectives: Introduction of post-mortem imaging has helped improve sudden unexpected death in infancy (SUDI) management in Europe. French guidelines were issued in 2007 to homogenise SUDI investigations including imaging. The aim of this study was to evaluate current imaging management of SUDI in France. Material and methods: Between January 2022 and July 2022, all 35 SUDI French referral centres were invited to answer an e-mailed online survey including 29 questions divided into four different sections covering imaging practices for SUDI including radiology department organisation, imaging modalities performed, methods of reading, and current training resources. Partial responses were secondarily completed by a personal call to the SUDI imaging consultant. The current implementation of the 2007 recommendations was compared with a previous evaluation from 2015 and with current North American practices. Results: The participation rate of centres performing SUDI imaging was 100% (35/35). Imaging was systematically performed in 94.3% (33/35) of the centres: 74.3% (26/35) using radiography; 5.7% (2/35) using ultrasound; 94.3% (33/35) using computed tomography (CT), including 89% (31/35) whole-body CT and 5.7% (2/35) brain CT; and 20% (7/35) using magnetic resonance imaging (MRI). Two centres (5.7%, 2/35) did not systematically perform brain imaging. One (2.9%, 1/35) used ultrasound-guided biopsy. In comparison with 2015, rates of brain imaging increased by 25.4% (P=0.008). There was no significant difference in the number of forensic MRIs performed between France and North America (P=0.663). Conclusion: Despite improvements since 2015, full compliance with French guidelines for SUDI investigations remains incomplete. The use of imaging, particularly CT and brain imaging, has increased. Further efforts are needed to standardise imaging practices for optimal SUDI investigations. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate (1) the short-term evolution of pain and (2) the treatment success of full pulpotomy as permanent treatment of irreversible pulpitis in mature molars. The study consisted of a non-randomized comparison between a test group (n = 44)—full pulpotomy performed by non-specialist junior practitioners, and a control group (n = 40)—root canal treatments performed by specialized endodontists. Short-term pain score (Heft–Parker scale) was recorded pre-operatively, then at 24 h and 7 days post-operatively. Three outcomes were considered for treatment success: radiographic, clinical and global success. For short-term evolution of pain, a non-parametric Wilcoxon test was performed (significance level = 0.05). For treatment success, a Pearson Chi square or Fisher test were performed (significance level = 0.017–Bonferroni correction). There was no significant difference between test and control groups neither regarding short term evolution of pain at each time point, nor regarding clinical (80% and 90%, respectively) or global success (77% and 67%, respectively). However, a significant difference in radiographic success was observed (94% and 69%, respectively). The present work adds to the existing literature to support that pulpotomy as permanent treatment could be considered as an acceptable and conservative treatment option, potentially applied by a larger population of dentists.

Concerns exist that the positive association of physical activity with better lung function, which has been suggested in previous longitudinal studies in smokers, is due to reverse causation. To investigate this, we applied structural equation modeling (SEM), an exploratory approach, and marginal structural modeling (MSM), an approach from the causal inference framework that corrects for reverse causation and time-dependent confounding and estimates causal effects, on data from participants in the European Community Respiratory Health Survey (ECRHS, a multicentre European cohort study initiated in 1991-1993 with ECRHS I, and with two follow-ups: ECRHS II in 1999-2003, and ECRHS III in 2010-2014). 753 subjects who reported current smoking at ECRHS II, with repeated data on lung function at ECRHS I, II and III, physical activity at ECRHS II and III, and potential confounders at ECRHS I and II, were included in the analyses. SEM showed positive associations between physical activity and lung function in both directions. MSM suggested a protective causal effect of physical activity on lung function (overall difference in mean β (95% CI), comparing active versus non-active individuals: 58 mL (21-95) for forced expiratory volume in one second and 83 mL (36-130) for forced vital capacity). Our results suggest bi-directional causation and support a true protective effect of physical activity on lung function in smokers, after accounting for reverse causation and time-dependent confounding.