Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

BACKGROUND: Wrist-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. OBJECTIVE: The 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. METHODS: Participants 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. RESULTS: The 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. CONCLUSIONS: Algorithms 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 REGISTRATION: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-050785.

Physical activity and body mass related to catch-up lung function growth in childhood: a population-based accelerated cohort study.

OBJECTIVE: The existence of catch-up lung function growth and its predictors is uncertain. We aimed to identify lung function trajectories and their predictors in a population-based birth cohort. METHODS: We applied group-based trajectory modelling to z-scores of forced expiratory volume in 1 second (zFEV1) and z-scores of forced vital capacity (zFVC) from 1151 children assessed at around 4, 7, 9, 10, 11, 14 and 18 years. Multinomial logistic regression models were used to test whether potential prenatal and postnatal predictors were associated with lung function trajectories. RESULTS: We identified four lung function trajectories: a low (19% and 19% of the sample for zFEV1 and zFVC, respectively), normal (62% and 63%), and high trajectory (16% and 13%) running in parallel, and a catch-up trajectory (2% and 5%) with catch-up occurring between 4 and 10 years. Fewer child allergic diseases and higher body mass index z-score (zBMI) at 4 years were associated with the high and normal compared with the low trajectories, both for zFEV1 and zFVC. Increased children's physical activity during early childhood and higher zBMI at 4 years were associated with the catch-up compared with the low zFEV1 trajectory (relative risk ratios: 1.59 per physical activity category (1.03-2.46) and 1.47 per zBMI (0.97-2.23), respectively). No predictors were identified for zFVC catch-up growth. CONCLUSION: We found three parallel-running and one catch-up zFEV1 and zFVC trajectories, and identified physical activity and body mass at 4 years as predictors of zFEV1 but not zFVC catch-up growth.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

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.

Spirometric patterns in young and middle-aged adults: a 20-year European study.

BACKGROUND: Understanding the natural history of abnormal spirometric patterns at different stages of life is critical to identify and optimise preventive strategies. We aimed to describe characteristics and risk factors of restrictive and obstructive spirometric patterns occurring before 40 years (young onset) and between 40 and 61 years (mid-adult onset). METHODS: We used data from the population-based cohort of the European Community Respiratory Health Survey (ECRHS). Prebronchodilator forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were assessed longitudinally at baseline (ECRHS1, 1993-1994) and again 20 years later (ECRHS3, 2010-2013). Spirometry patterns were defined as: restrictive if FEV1/FVC≥LLN and FVC<10th percentile, obstructive if FEV1/FVC

Early life exposures contributing to accelerated lung function decline in adulthood - a follow-up study of 11,000 adults from the general population.

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.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The 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. Methods: Here, 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 discussion: The 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 <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

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.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

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.

Changes in Population Health-Related Behaviors During a COVID-19 Surge: A Natural Experiment.

BACKGROUND: The study of impact of lockdowns on individual health-related behaviors has produced divergent results. PURPOSE: To identify patterns of change in multiple health-related behaviors analyzed as a whole, and their individual determinants. METHODS: Between March and August 2020, we collected data on smoking, alcohol, physical activity, weight, and sleep in a population-based cohort from Catalonia who had available pre-pandemic data. We performed multiple correspondence and cluster analyses to identify patterns of change in health-related behaviors and built multivariable multinomial logistic regressions to identify determinants of behavioral change. RESULTS: In 10,032 participants (59% female, mean (SD) age 55 (8) years), 8,606 individuals (86%) modified their behavior during the lockdown. We identified five patterns of behavioral change that were heterogeneous and directed both towards worsening and improvement in diverse combinations. Patterns ranged from "global worsening" (2,063 participants, 21%) characterized by increases in smoking, alcohol consumption, and weight, and decreases in physical activity levels and sleep time, to "improvement" (2,548 participants, 25%) characterized by increases in physical activity levels, decreases in weight and alcohol consumption, and both increases and decreases in sleep time. Being female, of older age, teleworking, having a higher education level, assuming caregiving responsibilities, and being more exposed to pandemic news were associated with changing behavior (all p < .05), but did not discriminate between favorable or unfavorable changes. CONCLUSIONS: Most of the population experienced changes in health-related behavior during lockdowns. Determinants of behavior modification were not explicitly associated with the direction of changes but allowed the identification of older, teleworking, and highly educated women who assumed caregiving responsibilities at home as susceptible population groups more vulnerable to lockdowns.

Lockdowns implemented during the first surge of the COVID-19 pandemic created highly disruptive scenarios impacting many aspects of life, including health-related behaviors. While early studies on isolated health-related behaviors partly aid in the understanding of changes in some of these behaviors, there is robust evidence supporting the idea that health-related behaviors and their changes often co-occur and should be studied and analyzed as a whole. Hence, in this study, we used hypothesis-free methods to identify inter-dependent patterns of change in health-related behaviors including tobacco smoking, alcohol consumption, physical activity, sleep, and weight in a population-based sample of 10,032 adults from Catalonia, Spain. We found that 86% of participants modified their health-related behavior during the lockdown as we identified five patterns of behavioral change, ranging from general worsening to improvement, in diverse combinations. Additionally, we found that being female, older age, teleworking, highly educated, assuming caregiving responsibilities, and having a high exposure to pandemic news were main the determinants of patterns characterized by changing behaviors (both worsening and improving). Overall, our results highlight the heterogeneity, co-occurrence, and inter-play between health-related behaviors under a natural experiment, and identify common demographic, socio-environmental and behavioral factors that might predict changes in behavior.

Creatine Kinase Is Decreased in Childhood Asthma.

Rationale: The identification of novel molecules associated with asthma may provide insights into the mechanisms of disease and their potential clinical implications. Objectives: To conduct a screening of circulating proteins in childhood asthma and to study proteins that emerged from human studies in a mouse model of asthma. Methods: We included 2,264 children from eight birth cohorts from the Mechanisms of the Development of ALLergy project and the Tucson Children's Respiratory Study. In cross-sectional analyses, we tested 46 circulating proteins for association with asthma in the selection stage and carried significant signals forward to a validation and replication stage. As CK (creatine kinase) was the only protein consistently associated with asthma, we also compared whole blood CK gene expression between subjects with and without asthma (n = 249) and used a house dust mite (HDM)-challenged mouse model to gain insights into CK lung expression and its role in the resolution of asthma phenotypes. Measurements and Main Results: As compared with the lowest CK tertile, children in the highest tertile had significantly lower odds for asthma in selection (adjusted odds ratio, 95% confidence interval: 0.31; 0.15-0.65; P = 0.002), validation (0.63; 0.42-0.95; P = 0.03), and replication (0.40; 0.16-0.97; P = 0.04) stages. Both cytosolic CK forms (CKM and CKB) were underexpressed in blood from asthmatics compared with control subjects (P = 0.01 and 0.006, respectively). In the lungs of HDM-challenged mice, Ckb expression was reduced, and after the HDM challenge, a CKB inhibitor blocked the resolution of airway hyperresponsiveness and reduction of airway mucin. Conclusions: Circulating concentrations and gene expression of CK are inversely associated with childhood asthma. Mouse models support a possible direct involvement of CK in asthma protection via inhibition of airway hyperresponsiveness and reduction of airway mucin.

Long-term effect of asthma on the development of obesity among adults: an international cohort study, ECRHS.

INTRODUCTION: Obesity is a known risk factor for asthma. Although some evidence showed asthma causing obesity in children, the link between asthma and obesity has not been investigated in adults. METHODS: We used data from the European Community Respiratory Health Survey (ECRHS), a cohort study in 11 European countries and Australia in 3 waves between 1990 and 2014, at intervals of approximately 10 years. We considered two study periods: from ECRHS I (t) to ECRHS II (t+1), and from ECRHS II (t) to ECRHS III (t+1). We excluded obese (body mass index≥30 kg/m2) individuals at visit t. The relative risk (RR) of obesity at t+1 associated with asthma at t was estimated by multivariable modified Poisson regression (lag) with repeated measurements. Additionally, we examined the association of atopy and asthma medication on the development of obesity. RESULTS: We included 7576 participants in the period ECRHS I-II (51.5% female, mean (SD) age of 34 (7) years) and 4976 in ECRHS II-III (51.3% female, 42 (8) years). 9% of participants became obese in ECRHS I-II and 15% in ECRHS II-III. The risk of developing obesity was higher among asthmatics than non-asthmatics (RR 1.22, 95% CI 1.07 to 1.38), and particularly higher among non-atopic than atopic (1.47; 1.17 to 1.86 vs 1.04; 0.86 to 1.27), those with longer disease duration (1.32; 1.10 to 1.59 in >20 years vs 1.12; 0.87 to 1.43 in ≤20 years) and those on oral corticosteroids (1.99; 1.26 to 3.15 vs 1.15; 1.03 to 1.28). Physical activity was not a mediator of this association. CONCLUSION: This is the first study showing that adult asthmatics have a higher risk of developing obesity than non-asthmatics, particularly those non-atopic, of longer disease duration or on oral corticosteroids.

Mid-childhood fat mass and airflow limitation at 15 years: The mediating role of insulin resistance and C-reactive protein.

BACKGROUND: We previously reported an association of high fat mass levels from age 9 to 15 years with lower forced expiratory flow in 1 s (FEV1 )/forced vital capacity (FVC) ratio (i.e., increased risk of airflow limitation) at 15 years. Here, we aimed to assess whether insulin resistance and C-reactive protein (CRP) at 15 years partially mediate this association. METHODS: We included 2263 children from the UK Avon Longitudinal Study of Parents and Children population-based cohort (ALSPAC). Four fat mass index (FMI) trajectories ("low," "medium-low," "medium-high," "high") from 9 to 15 years were previously identified using Group-Based Trajectory Modeling. Data on CRP, glucose, insulin, and post-bronchodilator FEV1 /FVC were available at 15 years. We defined insulin resistance by the homeostasis model assessment-estimated insulin resistance index (HOMA-IR). We used adjusted linear regression models and a causal mediation analysis to assess the mediating role of HOMA-IR and CRP. RESULTS: Compared to children in the "low" FMI trajectory, children in the "medium-high" and "high" FMI trajectories had lower FEV1 /FVC at 15 years. The percentage of the total effect explained by HOMA-IR was 19.8% [-114.1 to 170.0] and 20.4% [1.6 to 69.0] for the "medium-high" and "high" trajectories, respectively. In contrast, there was little evidence for a mediating role of CRP. CONCLUSION: The association between mid-childhood fat mass and FEV1 /FVC ratio at 15 years may be partially mediated by insulin resistance.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

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.

Gestational phthalate exposure and lung function during childhood: A prospective population-based study.

The potential effect of gestational exposure to phthalates on the lung function levels during childhood is unclear. Therefore, we examined this association at different ages (from 4 to 11 years) and over the whole childhood. Specifically, we measured 9 phthalate metabolites (MEP, MiBP, MnBP, MCMHP, MBzP, MEHHP, MEOHP, MECPP, MEHP) in the urine of 641 gestating women from the INMA study (Spain) and the forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and FEV1/FVC in their offspring at ages 4, 7, 9 and 11. We used linear regression and mixed linear regression with a random intercept for subject to assess the association between phthalates and lung function at each study visit and for the overall childhood, respectively. We also assessed the phthalate metabolites mixture effect on lung function using a Weighted Quantile Sum (WQS) regression. We observed that the phthalate metabolites gestational levels were consistently associated with lower FVC and FEV1 at all ages, both when assessed individually and jointly as a mixture, although most associations were not statistically significant. Of note, a 10% increase in MiBP was related to lower FVC (-0.02 (-0.04, 0)) and FEV1 z-scores (-0.02 (-0.04, -0.01) at age 4. Similar significant reductions in FVC were observed at ages 4 and 7 associated with an increase in MEP and MnBP, respectively, and for FEV1 at age 4 associated with an increase in MBzP. WQS regression consistently identified MBzP as an important contributor to the phthalate mixture effect. We can conclude that the gestational exposure to phthalates was associated with children's lower FVC and FEV1, especially in early childhood, and in a statistically significant manner for MEP, MiBP, MBzP and MnBP. Given the ubiquity of phthalate exposure and its established endocrine disrupting effects in children, our findings support current regulations that limit phthalate exposure.

Urban environment and physical activity and capacity in patients with chronic obstructive pulmonary disease.

BACKGROUND: Physical activity and exercise capacity are key prognostic factors in chronic obstructive pulmonary disease (COPD) but their environmental determinants are unknown. OBJECTIVES: To test the association between urban environment and objective physical activity, physical activity experience and exercise capacity in COPD. METHODS: We studied 404 patients with mild-to-very severe COPD from a multi-city study in Catalonia, Spain. We measured objective physical activity (step count and sedentary time) by the Dynaport MoveMonitor, physical activity experience (difficulty with physical activity) by the Clinical visit-PROactive (C-PPAC) instrument, and exercise capacity by the 6-min walk distance (6MWD). We estimated individually (geocoded to the residential address) population density, pedestrian street length, slope of terrain, and long-term (i.e., annual) exposure to road traffic noise, nitrogen dioxide (NO2) and particulate matter (PM2.5). We built single- and multi-exposure mixed-effects linear regressions with a random intercept for city, adjusting for confounders. RESULTS: Patients were 85% male, had mean (SD) age 69 (9) years and walked 7524 (4045) steps/day. In multi-exposure models, higher population density was associated with fewer steps, more sedentary time and worse exercise capacity (-507 [95% CI: 1135, 121] steps, +0.2 [0.0, 0.4] h/day and -13 [-25, 0] m per IQR). Pedestrian street length related with more steps and less sedentary time (156 [9, 304] steps and -0.1 [-0.1, 0.0] h/day per IQR). Steeper slope was associated with better exercise capacity (15 [3, 27] m per IQR). Higher NO2 levels related with more sedentary time and more difficulty in physical activity. PM2.5 and noise were not associated with physical activity or exercise capacity. DISCUSSION: Population density, pedestrian street length, slope and NO2 exposure relate to physical activity and capacity of COPD patients living in highly populated areas. These findings support the consideration of neighbourhood environmental factors during COPD management and the attention to patients with chronic diseases when developing urban and transport planning policies.

Higher mortality and intubation rate in COVID-19 patients treated with noninvasive ventilation compared with high-flow oxygen or CPAP.

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.

Multisite greenness exposure and oxidative stress in children. The potential mediating role of physical activity.

Residential greenness exposure has been reported to positively impact health mainly by reducing overweight/obesity risk, improving mental health and physical activity. Less is known on biological pathways involved in these health benefits. We aimed to investigate the association between multisite greenness exposure and oxidative stress in children and explore the potential mediating role of physical activity in this association. This cross-sectional study involved 323 healthy subjects (8-11 y) from five schools in Asti (Italy). Children's parents filled a questionnaire providing the residential address, parental education, and physical activity frequency. Oxidative stress was quantified in spot urine by isoprostane (15-F2t-IsoP) using ELISA technique. Residential and scholastic greenness were defined by the Normalized Difference Vegetation Index (NDVI) in buffers with 100, 250, 300, 500 and 1000 m radii, and vegetated portion was also estimated. Multisite exposures were derived accounting for NDVI around home and school, weighted for time spent in each location. Linear mixed models, age-adjusted, with schools as random intercept, tested the association between 500 m-radius buffer multisite grenness variables and log (15-F2t-IsoP), reporting decreased oxidative stress per interquartile range (IQR) increase in multisite NDVI (ß: 012, 95%CI -0.240 to 0.004) and multisite vegetated portion (ß: 0.14, 95%CI -0.270 to -0.006). The mediation analysis did not support the hypothesis that physical activity frequency could mediate these associations. Multisite greenness exposure is associated with decreased oxidative stress in children and our data did not support the mediating role of physical activity.

Maternal sleep duration and neonate birth weight: A population-based cohort study.

OBJECTIVE: To investigate the association between maternal sleep duration (an important health indicator) and neonate birth weight. METHODS: The study included 2536 mother-neonate pairs of a Spanish birth cohort (2004-2006, INMA project). The exposures were questionnaire-based measures of sleep duration before and during pregnancy. The primary outcome was neonate birth weight score (g) standardized to 40 weeks of gestation. RESULTS: In women sleeping for <7 h/day before pregnancy, each additional hour of sleep increased birth weight score by 44.7 g (P = 0.049) in the minimally adjusted model, although findings were not statistically significant after considering other potential confounders (P > 0.05). However, increasing sleep duration for the group of mothers who slept for more than 9 h/day decreased birth weight score by 39.2 g per additional hour (P = 0.001). Findings were similar after adjusting for several sociodemographic confounders and maternal depression-anxiety clinical history as an intermediate factor. Similar but attenuated associations were observed with sleep duration in the second trimester of pregnancy. CONCLUSION: The relationship between maternal sleep duration before and during pregnancy and neonate birth weight is an inverse U-shaped curve. Excessive sleep duration may adversely affect neonate health through its impact on birth weight.

Technical validation of real-world monitoring of gait: a multicentric observational study.

INTRODUCTION: Existing 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 ANALYSIS: This 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 DISSEMINATION: The 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 NUMBER: ISRCTN (12246987).