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.

Short-term effects of air pollution and weather on physical activity in patients with chronic obstructive pulmonary disease (COPD).

INTRODUCTION: Patients with chronic obstructive pulmonary disease (COPD) accumulate low levels of physical activity. How environmental factors affect their physical activity in the short-term is uncertain. AIM: to assess the short-term effects of air pollution and weather on physical activity levels in COPD patients. METHODS: This multi-center panel study assessed 408 COPD patients from Catalonia (Spain). Daily physical activity (i.e., steps, time in moderate-to-vigorous physical activity (MVPA), locomotion intensity, and sedentary time) was recorded in two 7-day periods, one year apart, using the Dynaport MoveMonitor. Air pollution (nitrogen dioxide (NO2), particulate matter below 10 µm (PM10) and a marker of black carbon (absorbance of PM2.5: PM2.5ABS), and weather (average and maximum temperature, and rainfall) were estimated the same day (lag zero) and up to 5 days prior to each assessment (lags 1-5). Mixed-effect distributed lag linear regression models were adjusted for age, sex, weekday, public holidays, greenness, season, and social class, with patient and city as random effects. RESULTS: Patients (85% male) were on average (mean ± SD) 68 ± 9 years old with a post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57 ± 18% predicted. Higher NO2, PM10 and PM2.5ABS levels at lag four were associated with fewer steps, less time in MVPA, reduced locomotion intensity, and longer sedentary time (e.g., coefficient (95% CI) of -60 (-105, -15) steps per 10 µg/m3 increase in NO2). Higher average and maximum temperatures at lag zero were related to more steps and time in MVPA, and less sedentary time (e.g., +85 (15, 154) steps per degree Celsius). Higher rainfall at lag zero was related to fewer steps and more sedentary time. CONCLUSION: Air pollution affects the amount and intensity of physical activity performed on the following days in COPD patients, whereas weather affects the amount of physical activity performed on the same day.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts.

BACKGROUND: Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes. OBJECTIVE: To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts. METHODS: We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome. RESULTS: In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis. CONCLUSIONS: No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Simultaneous but independent spatial associations for pitch and loudness.

For the auditory dimensions loudness and pitch a vertical SARC effect (Spatial Association of Response Codes) exists: When responding to loud (high) tones, participants are faster with top-sided responses compared to bottom-sided responses and vice versa for soft (low) tones. These effects are typically explained by two different spatial representations for both dimensions with pitch being represented on a helix structure and loudness being represented as spatially associated magnitude. Prior studies show incoherent results with regard to the question whether two SARC effects can occur at the same time as well as whether SARC effects interact with each other. Therefore, this study aimed to investigate the interrelation between the SARC effect for pitch and the SARC effect for loudness in a timbre discrimination task. Participants (N = 36) heard one tone per trial and had to decide whether the presented tone was a violin tone or an organ tone by pressing a top-sided or bottom-sided response key. Loudness and pitch were varied orthogonally. We tested the occurrence of SARC effects for pitch and loudness as well as their potential interaction by conducting a multiple linear regression with difference of reaction time (dRT) as dependent variable, and loudness and pitch as predictors. Frequentist and Bayesian analyses revealed that the regression coefficients of pitch and loudness were smaller than zero indicating the simultaneous occurrence of a SARC effects for both dimensions. In contrast, the interaction coefficient was not different from zero indicating an additive effect of both predictors.

Gait differences between COPD and healthy controls: systematic review and meta-analysis.

BACKGROUND: Despite the importance of gait as a determinant of falls, disability and mortality in older people, understanding of gait impairment in COPD is limited. This study aimed to identify differences in gait characteristics during supervised walking tests between people with COPD and healthy controls. METHODS: We searched 11 electronic databases, supplemented by Google Scholar searches and manual collation of references, in November 2019 and updated the search in July 2021. Record screening and information extraction were performed independently by one reviewer and checked for accuracy by a second. Meta-analyses were performed in studies not considered at a high risk of bias. RESULTS: Searches yielded 21 085 unique records, of which 25 were included in the systematic review (including 1015 people with COPD and 2229 healthy controls). Gait speed was assessed in 17 studies (usual speed: 12; fast speed: three; both speeds: two), step length in nine, step duration in seven, cadence in six, and step width in five. Five studies were considered at a high risk of bias. Low-quality evidence indicated that people with COPD walk more slowly than healthy controls at their usual speed (mean difference (MD) -19 cm·s-1, 95% CI -28 to -11 cm·s-1) and at a fast speed (MD -30 cm·s-1, 95% CI -47 to -13 cm·s-1). Alterations in other gait characteristics were not statistically significant. CONCLUSION: Low-quality evidence shows that people with COPD walk more slowly than healthy controls, which could contribute to an increased falls risk. The evidence for alterations in spatial and temporal components of gait was inconclusive. Gait impairment appears to be an important but understudied area in COPD.

Prognostic impact of CEBPA mutational subgroups in adult AML.

Despite recent refinements in the diagnostic and prognostic assessment of CEBPA mutations in AML, several questions remain open, i.e. implications of different types of basic region leucin zipper (bZIP) mutations, the role of co-mutations and the allelic state. Using pooled primary data analysis on 1010 CEBPA-mutant adult AML patients, a comparison was performed taking into account the type of mutation (bZIP: either typical in-frame insertion/deletion (InDel) mutations (bZIPInDel), frameshift InDel or nonsense mutations inducing translational stop (bZIPSTOP) or single base-pair missense alterations (bZIPms), and transcription activation domain (TAD) mutations) and the allelic state (single (smCEBPA) vs. double mutant (dmCEBPA)). Only bZIPInDel patients had significantly higher rates of complete remission and longer relapse free and overall survival (OS) compared with all other CEBPA-mutant subgroups. Moreover, co-mutations in bZIPInDel patients (e.g. GATA2, FLT3, WT1 as well as ELN2022 adverse risk aberrations) had no independent impact on OS, whereas in non-bZIPInDel patients, grouping according to ELN2022 recommendations added significant prognostic information. In conclusion, these results demonstrate bZIPInDel mutations to be the major independent determinant of outcome in CEBPA-mutant AML, thereby refining current classifications according to WHO (including all dmCEBPA and smCEBPA bZIP) as well as ELN2022 and ICC recommendations (including CEBPA bZIPms).

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.

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.