Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

The neighbourhood environment and profiles of the metabolic syndrome.

BACKGROUND: There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components. METHODS: We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles. RESULTS: LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS. CONCLUSIONS: This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

Does surrounding greenness moderate the relationship between apparent temperature and physical activity? Findings from the PHENOTYPE project.

BACKGROUND: Physical activity can be affected by both meteorological conditions and surrounding greenness, but few studies have evaluated the effects of these environmental factors on physical activity simultaneously. This multi-city comparative study aimed to assess the synergetic effects of apparent temperature and surrounding greenness on physical activity in four European cities. Specifically, we aimed to identify an interaction between surrounding greenness and apparent temperature in the effects on physical activity. METHODS: Data were collected from 352 adult residents of Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands), and Kaunas (Lithuania) as part of the PHENOTYPE study. Participants wore a smartphone for seven consecutive days between May-December 2013 and provided additional sociodemographic survey data. Hourly average physical activity (Metabolic Equivalent of Task (MET)) and surrounding greenness (NDVI) were derived from the Calfit mobile application collecting accelerometer and location data. Hourly apparent temperature was calculated from temperature and relative humidity, which were obtained from local meteorological stations along with other meteorological covariates (rainfall, windspeed, and sky darkness). We assessed the interaction effects of apparent temperature and surrounding greenness on hourly physical activity for each city using linear mixed models, while adjusting for meteorological, demographic, and time-related variables. RESULTS: We found significant interactions between apparent temperature and surrounding greenness on hourly physical activity in three of four cities, aside from the coastal city of Barcelona. Significant quadratic effects of apparent temperature were found in the highest level of surrounding greenness for Stoke-on-Trent and Doetinchem, with 4% decrease in median MET observed for a 10°C departure from optimal temperature (15.2°C and 14.6°C, respectively). Significant linear effects were found for higher levels of surrounding greenness in Kaunas, whereby an increase of 10°C was associated with ∼4% increase in median MET. CONCLUSION: Apparent temperature and surrounding greenness interacted in the effect on hourly physical activity across three of four European cities, with varying effect between cities. While quadratic effects of temperature suggest diminishing levels of physical activity in the highest greenness levels in cities of temperate climates, the variation in surrounding greenness between cities could be further explored, particularly by looking at indoor-outdoor locations. The study findings support the need for evidence-based physical activity promotion and urban design.

Physical and mental health effects of repeated short walks in a blue space environment: A randomised crossover study.

INTRODUCTION: Blue spaces may benefit mental health and promote physical activity, although the evidence is still scarce. And benefits on physical health are less consistent. The objective of this randomized crossover study was to assess psychological and cardiovascular responses to blue spaces' exposure. METHODS: A sample of 59 healthy adult office workers was randomly assigned to a different environment (i.e. blue space, urban space, and control site) on 4 days each week, for 3 weeks. For 20 min per day, they either walked along a blue or an urban space or rested at a control site. Before, during and/or after the exposure, we measured self-reported well-being and mood, blood pressure, and heart rate variability parameters. For well-being, we also assessed the duration of these potential effects over time (at least 4 h after exposure). RESULTS: We found significantly improved well-being and mood responses immediately after walking in the blue space compared with walking in the urban space or when resting in the control site. Cardiovascular responses showed increased activity of the sympathetic nervous system, both during and after walking along the blue and urban spaces. However, cardiovascular responses measured after the walks, showed no statistically significant differences between the blue and the urban space environments. CONCLUSIONS: Short walks in blue spaces can benefit both well-being and mood. However, we did not observe a positive effect of blue spaces for any of the cardiovascular outcomes assessed in this study.

Telecommunication devices use, screen time and sleep in adolescents.

PURPOSE: To investigate the association between telecommunication and other screen devices use and subjective and objective sleep measures in adolescents at 17-18 years. METHODS: Cross-sectional study on adolescents aged 17-18 years from a Spanish population-based birth cohort established in Menorca in 1997-1998. Information on devices use was collected using self-reported questionnaires. Mobile Phone Problematic Use Scale was used to assess mobile phone use dependency. Pittsburgh Sleep Quality Index was used to assess subjective sleep (n = 226). ActiGraph wGT3X-BT for 7 nights was used to assess objective sleep (n = 110). RESULTS: One or more cordless phone calls/week was associated with a lower sleep quality [Prevalence Ratio (PR) 1.30 (95%Confidence Interval (CI) 1.04; 1.62)]. Habitual and frequent problematic mobile phone use was associated with a lower sleep quality [PR 1.55 (95%CI 1.03; 2.33) and PR 1.67 (95%CI 1.09; 2.56), respectively]. Higher tablet use was associated with decreased sleep efficiency and increased minutes of wake time after sleep onset [ß-1.15 (95%CI -1.99; -0.31) and ß 7.00 (95%CI 2.40; 11.60) per increase of 10 min/day of use, respectively]. No associations were found between other devices and sleep measures. CONCLUSIONS: Frequency of cordless phone calls, mobile phone dependency, and tablet use were related to an increase of subjective and objective sleep problems in adolescents. These results seem to indicate that sleep displacement, mental arousal, and exposure to blue light screen emission might play a more important role on sleep than a high RF-EMF exposure to the brain. However, more studies are needed assessing personal RF-EMF levels to draw conclusions.

Personal assessment of the external exposome during pregnancy and childhood in Europe.

The human exposome affects child development and health later in life, but its personal external levels, variability, and correlations are largely unknown. We characterized the personal external exposome of pregnant women and children in eight European cities. Panel studies included 167 pregnant women and 183 children (aged 6-11 years). A personal exposure monitoring kit composed of smartphone, accelerometer, ultraviolet (UV) dosimeter, and two air pollution monitors were used to monitor physical activity (PA), fine particulate matter (PM2.5), black carbon, traffic-related noise, UV-B radiation, and natural outdoor environments (NOE). 77% of women performed the adult recommendation of ≥150 min/week of moderate to vigorous PA (MVPA), while only 3% of children achieved the childhood recommendation of ≥60 min/day MVPA. 11% of women and 17% of children were exposed to daily PM2.5 levels higher than recommended (≥25µg/m3). Mean exposure to noise ranged from Lden 51.1 dB in Kaunas to Lden 65.2 dB in Barcelona. 4% of women and 23% of children exceeded the recommended maximum of 2 Standard-Erythemal-Dose of UV-B at least once a week. 33% of women and 43% of children never reached the minimum NOE contact recommendation of ≥30 min/week. The variations in air and noise pollution exposure were dominated by between-city variability, while most of the variation observed for NOE contact and PA was between-participants. The correlations between all personal exposures ranged from very low to low (Rho < 0.30). The levels of personal external exposures in both pregnant women and children are above the health recommendations, and there is little correlation between the different exposures. The assessment of the personal external exposome is feasible but sampling requires from one day to more than one year depending on exposure due to high variability between and within cities and participants.

The dyspnoea-inactivity vicious circle in COPD: development and external validation of a conceptual model.

The vicious circle of dyspnoea-inactivity has been proposed, but never validated empirically, to explain the clinical course of chronic obstructive pulmonary disease (COPD). We aimed to develop and validate externally a comprehensive vicious circle model.We utilised two methods. 1) Identification and validation of all published vicious circle models by a systematic literature search and fitting structural equation models to longitudinal data from the Spanish PAC-COPD (Phenotype and Course of COPD) cohort (n=210, mean age 68 years, mean forced expiratory volume in 1 s (FEV1) 54% predicted), testing both the hypothesised relationships between variables in the model ("paths") and model fit. 2) Development of a new model and external validation using longitudinal data from the Swiss and Dutch ICE COLD ERIC (International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts) cohort (n=226, mean age 66 years, mean FEV1 57% predicted).We identified nine vicious circle models for which structural equation models confirmed most hypothesised paths but showed inappropriate fit. In the new model, airflow limitation, hyperinflation, dyspnoea, physical activity, exercise capacity and COPD exacerbations remained related to other variables and model fit was appropriate. Fitting it to ICE COLD ERIC, all paths were replicated and model fit was appropriate.Previously published vicious circle models do not fully explain the vicious circle concept. We developed and externally validated a new comprehensive model that gives a more relevant role to exercise capacity and COPD exacerbations.

Land use regression models for the oxidative potential of fine particles (PM2.5) in five European areas.

Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m-3) of OPAA and OPGSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OPGSH); the Netherlands; and Turin, IT) using PM2.5 filters. OPAA and OPGSH LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OPAA and OPGSH were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM2.5 and other metrics (PM2.5absorbance, NO2, Cu, Fe). HOV (hold-out validation) R2 for OPAA models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OPGSH, the only model achieving at least moderate performance was for the Netherlands (R2 = .31). Combined models for OPAA and OPGSH were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OPAA with other pollutants, the three reasonably performing LUR models for OPAA could be used independently of other pollutant metrics in epidemiological studies.

Analysis of nocturnal actigraphic sleep measures in patients with COPD and their association with daytime physical activity.

BACKGROUND: Sleep disturbances are common in patients with chronic obstructive pulmonary disease (COPD) with a considerable negative impact on their quality of life. However, factors associated with measures of sleep in daily life have not been investigated before nor has the association between sleep and the ability to engage in physical activity on a day-to-day basis been studied. AIMS: To provide insight into the relationship between actigraphic sleep measures and disease severity, exertional dyspnoea, gender and parts of the week; and to investigate the association between sleep measures and next day physical activity. METHODS: Data were analysed from 932 patients with COPD (66% male, 66.4±8.3 years, FEV1% predicted=50.8±20.5). Participants had sleep and physical activity continuously monitored using a multisensor activity monitor for a median of 6 days. Linear mixed effects models were applied to investigate the factors associated with sleep impairment and the association between nocturnal sleep and patients' subsequent daytime physical activity. RESULTS: Actigraphic estimates of sleep impairment were greater in patients with worse airflow limitation and worse exertional dyspnoea. Patients with better sleep measures (ie, non-fragmented sleep, sleeping bouts ≥225 min, sleep efficiency ≥91% and time spent awake after sleep onset <57 min) spent significantly more time in light (p<0.01) and moderate-to-vigorous physical activity (p<0.01). CONCLUSIONS: There is a relationship between measures of sleep in patients with COPD and the amount of activity they undertake during the waking day. Identifying groups with specific sleep characteristics may be useful information when designing physical activity-enhancing interventions.

Land Use Regression Models for Ultrafine Particles in Six European Areas.

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Validating novel air pollution sensors to improve exposure estimates for epidemiological analyses and citizen science.

Low cost, personal air pollution sensors may reduce exposure measurement errors in epidemiological investigations and contribute to citizen science initiatives. Here we assess the validity of a low cost personal air pollution sensor. Study participants were drawn from two ongoing epidemiological projects in Barcelona, Spain. Participants repeatedly wore the pollution sensor - which measured carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2). We also compared personal sensor measurements to those from more expensive instruments. Our personal sensors had moderate to high correlations with government monitors with averaging times of 1-h and 30-min epochs (r ~ 0.38-0.8) for NO and CO, but had low to moderate correlations with NO2 (~0.04-0.67). Correlations between the personal sensors and more expensive research instruments were higher than with the government monitors. The sensors were able to detect high and low air pollution levels in agreement with expectations (e.g., high levels on or near busy roadways and lower levels in background residential areas and parks). Our findings suggest that the low cost, personal sensors have potential to reduce exposure measurement error in epidemiological studies and provide valid data for citizen science studies.

Natural outdoor environments and mental health: Stress as a possible mechanism.

INTRODUCTION: Better mental health has been associated with exposure to natural outdoor environments (NOE). However, comprehensive studies including several indicators of exposure and outcomes, potential effect modifiers and mediators are scarce. OBJECTIVES: We used novel, objective measures to explore the relationships between exposure to NOE (i.e. residential availability and contact) and different indicators of mental health, and possible modifiers and mediators. METHODS: A nested cross-sectional study was conducted in: Barcelona, Spain; Stoke-on-Trent, United Kingdom; Doetinchem, Netherlands; Kaunas, Lithuania. Participants' exposure to NOE (including both surrounding greenness and green and/or blue spaces) was measured in terms of (a) amount in their residential environment (using Geographical Information Systems) and (b) their contact with NOE (using smartphone data collected over seven days). Self-reported information was collected for mental health (psychological wellbeing, sleep quality, vitality, and somatisation), and potential effect modifiers (gender, age, education level, and city) and mediators (perceived stress and social contacts), with additional objective NOE physical activity (potential mediator) derived from smartphone accelerometers. RESULTS: Analysis of data from 406 participants showed no statistically significant associations linking mental health and residential NOE exposure. However, NOE contact, especially surrounding greenness, was statistically significantly tied to better mental health. There were indications that these relationships were stronger for males, younger people, low-medium educated, and Doetinchem residents. Perceived stress was a mediator of most associations, and physical activity and social contacts were not. CONCLUSIONS: Our findings indicate that contact with NOE benefits mental health. Our results also suggest that having contact with NOE that can facilitate stress reduction could be particularly beneficial.

Physical activity patterns and clusters in 1001 patients with COPD.

We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV1], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV1, worse dyspnoea and higher ADO index compared to other clusters ( p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.

Inspiratory capacity-to-total lung capacity ratio and dyspnoea predict exercise capacity decline in COPD.

BACKGROUND AND OBJECTIVE: Exercise capacity decline is a predictor of mortality in patients with chronic obstructive pulmonary disease (COPD). Static pulmonary hyperinflation is a key determinant of exercise performance, but its effect on the longitudinal decline in exercise capacity remains unknown. We aimed to study the relationship between the inspiratory capacity-to-total lung capacity (IC/TLC) ratio and exercise capacity decline in COPD. METHODS: We measured IC/TLC and other relevant clinical and functional variables in 342 clinically stable patients with COPD. The 6-min walk distance (6MWD) was determined at recruitment and after a mean ± SD of 1.7 ± 0.3 years. The annual rate of change in 6MWD was calculated. Multiple imputation to account for losses during follow up was implemented, and multivariate regression was used to analyze predictive factors of 6MWD decline. RESULTS: Mean decline rate in the 6MWD was 21.9 ± 34.1 m/year. In the bivariate analysis, patients with lower levels of IC/TLC had greater 6MWD decline (-27.4 ± 42.5, -24.9 ± 36.5 and -13.4 ± 39.9 m/year in the first, second and third tertile of IC/TLC, respectively; P-for-trend = 0.018). From other potential risk factors considered, dyspnoea, health status, serum C-reactive protein and Borg dyspnoea score at the end of the exercise test were related to exercise capacity decline. In the multivariate regression model, only IC/TLC (ß = 0.7 m/year per each percentage unit of IC/TLC; P = 0.007) and dyspnoea (mMRC ≥ 2) (ß = -14.6 m/year; P = 0.013) were associated with the annual rate of 6MWD change. CONCLUSION: IC/TLC and dyspnoea in clinically stable patients with COPD predict their exercise capacity decline and may help to guide early therapeutic interventions.

Benefits of physical activity on COPD hospitalisation depend on intensity.

The present study aims to disentangle the independent effects of the quantity and the intensity of physical activity on the risk reduction of chronic obstructive pulmonary disease (COPD) hospitalisations.177 patients from the Phenotype Characterization and Course of COPD (PAC-COPD) cohort (mean±sd age 71±8 years, forced expiratory volume in 1 s 52±16% predicted) wore the SenseWear Pro 2 Armband accelerometer (BodyMedia, Pittsburgh, PA, USA) for eight consecutive days, providing data on quantity (steps per day, physically active days and daily active time) and intensity (average metabolic equivalent tasks) of physical activity. Information on COPD hospitalisations during follow-up (2.5±0.8 years) was obtained from validated centralised datasets. During follow-up 67 (38%) patients were hospitalised. There was an interaction between quantity and intensity of physical activity in their effects on COPD hospitalisation risk. After adjusting for potential confounders in the Cox regression model, the risk of COPD hospitalisation was reduced by 20% (hazard ratio (HR) 0.79, 95% CI 0.67-0.93; p=0.005) for every additional 1000 daily steps at low average intensity. A greater quantity of daily steps at high average intensity did not influence the risk of COPD hospitalisations (HR 1.01, p=0.919). Similar results were found for the other measures of quantity of physical activity. Greater quantity of low-intensity physical activity reduces the risk of COPD hospitalisation, but high-intensity physical activity does not produce any risk reduction.

Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies.

Novel technologies, such as smartphones and small personal continuous air pollution sensors, can now facilitate better personal estimates of air pollution in relation to location. Such information can provide us with a better understanding about whether and how personal exposures relate to residential air pollution estimates, which are normally used in epidemiological studies. The aims of this study were to examine (1) the variability in personal air pollution levels during the day and (2) the relationship between modeled home and school estimates and continuously measured personal air pollution exposure levels in different microenvironments (e.g., home, school, and commute). We focused on black carbon as an indicator of traffic-related air pollution. We recruited 54 school children (aged 7-11) from 29 different schools around Barcelona as part of the BREATHE study, an epidemiological study of the relation between air pollution and brain development. For 2 typical week days during 2012-2013, the children were given a smartphone with CalFit software to obtain information on their location and physical activity level and a small sensor, the micro-aethalometer model AE51, to measure their black carbon levels simultaneously and continuously. We estimated their home and school exposure to PM2.5 filter absorbance, which is well-correlated with black carbon, using a temporally adjusted PM2.5 absorbance land use regression (LUR) model. We found considerable variation in the black carbon levels during the day, with the highest levels measured during commuting periods (geometric mean = 2.8 µg/m(3)) and the lowest levels at home (geometric mean = 1.3 µg/m(3)). Hourly temporally adjusted LUR model estimates for the home and school showed moderate to good correlation with measured personal black carbon levels at home and school (r = 0.59 and 0.68, respectively) and lower correlation with commuting trips (r = 0.32 and 0.21, respectively). The correlation between modeled home estimates and overall personal black carbon levels was 0.62. Personal black carbon levels vary substantially during the day. The correlation between modeled and measured black carbon levels was generally good, with the exception of commuting times. In conclusion, novel technologies, such as smartphones and sensors, provide insights in personal exposure to air pollution.

Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records.

Hip fractures (HFx) are associated with a higher morbidity and mortality rates, leading to a significant reduction in life quality and in limitation of patient´s mobility. The present study aimed to obtain real-world evidence on the clinical characteristics of patients with an initial and a second hip fracture (HFx) and develop a predictive model for second HFx using artificial intelligence. Electronic health records from one hospital centre in Spain from January 2011 to December 2019 were analysed using EHRead® technology, based on natural language processing and machine learning. A total of 1,960 patients with HFx were finally included during the study period after meeting all inclusion and exclusion criteria. From this total, 1835 (93.6%) patients were included in the HFx subgroup, while 124 (6.4%) were admitted to the second HFx (2HFx) subgroup. The mean age of the participants was 84 years and 75.5% were female. Most of comorbidities were more frequently identified in the HFx group, including hypertension (72.0% vs. 67.2%), cognitive impairment (33.0% vs. 31.2%), diabetes mellitus (28.7% vs. 24.8%), heart failure (27.6% vs. 22.4%) and chronic kidney disease (26.9% vs. 16.0%). Based on clinical criteria, 26 features were selected as potential prediction factors. From there, 16 demographics and clinical characteristics such as comorbidities, medications, measures of disabilities for ambulation and type of refracture were selected for development of a competitive risk model. Specifically, those predictors with different associated risk ratios, sorted from higher to lower risk relevance were visual deficit, malnutrition, walking assistance, hypothyroidism, female sex, osteoporosis treatment, pertrochanteric fracture, dementia, age at index, osteoporosis, renal failure, stroke, COPD, heart disease, anaemia, and asthma. This model showed good performance (dependent AUC: 0.69; apparent performance: 0.75) and could help the identification of patients with higher risk of developing a second HFx, allowing preventive measures. This study expands the current available information of HFx patients in Spain and identifies factors that exhibit potential in predicting a second HFx among older patients.

Physical activity in COPD patients: patterns and bouts.

The present study aims to describe the pattern of physical activity and the frequency, duration and intensity of physical activity bouts in patients with chronic obstructive pulmonary disease (COPD), to assess how these patterns differ according to COPD severity, and to explore whether these patients meet the general guidelines for physical activity for older adults. 177 patients (94% male, mean±sd age 71±8 years and forced expiratory volume in 1 s 52±16% predicted) wore the SenseWear Pro2 Armband accelerometer for eight consecutive days. Physical activity bouts were defined as periods of ≥10 min above 1.5 metabolic equivalent tasks and classified according to their median intensity. Patients engaged in activity a median of 153 min·day(-1) and 57% of that time was spent in bouts. Median frequencies of bouts per day were four and three for all and moderate-to-vigorous intensities, respectively. With increasing COPD severity, time in physical activity, proportion of time in bouts and frequency of bouts decreased. 61% of patients fulfilled the recommended physical activity guidelines. In conclusion, COPD patients of all spirometric severity stages engage in physical activity bouts of moderate-to-vigorous intensities. Patients with severe and very severe COPD perform their daily activities in fewer and shorter bouts than those in mild and moderate stages.

Comparison of physical activity measures using mobile phone-based CalFit and Actigraph.

BACKGROUND: Epidemiological studies on physical activity often lack inexpensive, objective, valid, and reproducible tools for measuring physical activity levels of participants. Novel sensing technologies built into smartphones offer the potential to fill this gap. OBJECTIVE: We sought to validate estimates of physical activity and determine the usability for large population-based studies of the smartphone-based CalFit software. METHODS: A sample of 36 participants from Barcelona, Spain, wore a smartphone with CalFit software and an Actigraph GT3X accelerometer for 5 days. The ease of use (usability) and physical activity measures from both devices were compared, including vertical axis counts (VT) and duration and energy expenditure predictions for light, moderate, and vigorous intensity from Freedson's algorithm. Statistical analyses included (1) Kruskal-Wallis rank sum test for usability measures, (2) Spearman correlation and linear regression for VT counts, (3) concordance correlation coefficient (CCC), and (4) Bland-Altman plots for duration and energy expenditure measures. RESULTS: Approximately 64% (23/36) of participants were women. Mean age was 31 years (SD 8) and mean body mass index was 22 kg/m(2) (SD 2). In total, 25/36 (69%) participants recorded at least 3 days with at least 10 recorded hours of physical activity using CalFit. The linear association and correlations for VT counts were high (adjusted R(2)=0.85; correlation coefficient .932, 95% CI 0.931-0.933). CCCs showed high agreement for duration and energy expenditure measures (from 0.83 to 0.91). CONCLUSIONS: The CalFit system had lower usability than the Actigraph GT3X because the application lacked a means to turn itself on each time the smartphone was powered on. The CalFit system may provide valid estimates to quantify and classify physical activity. CalFit may prove to be more cost-effective and easily deployed for large-scale population health studies than other specialized instruments because cell phones are already carried by many people.

Heart rate and oxygen uptake kinetics obtained from continuous measurements with wearable devices during outdoor walks of patients with COPD.

Objective: Continuous physiological measurements during a laboratory-based exercise test can provide physiological biomarkers, such as heart rate (HR) and oxygen uptake (V̇O2) kinetics, that carry clinically relevant information. In contrast, it is not clear how continuous data generated by wearable devices during daily-life routines could provide meaningful biomarkers. We aimed to determine whether valid HR and V̇O2 kinetics can be obtained from measurements with wearable devices during outdoor walks in patients with chronic obstructive pulmonary disease (COPD). Methods: HR (Polar Belt) and V̇O2(METAMAX3B) were measured during 93 physical activity transitions performed by eight patients with COPD during three different outdoor walks (ntr = 77) and a 6-minute walk test (ntr = 16). HR and V̇O2 kinetics were calculated every time a participant started a walk, finished a walk or walked upstairs. HR and V̇O2 kinetics were considered valid if the response magnitude and model fit were adequate, and model parameters were reliable. Results: Continuous measurements with wearable devices provided valid HR kinetics when COPD patients started or finished (range 63%-100%) the different outdoor walks and valid V̇O2 kinetics when they finished (range 63%-100%) an outdoor walk. The amount of valid kinetics and kinetic model performance was comparable between outdoor walks and a laboratory-based exercise test (p > .05). Conclusion: We envision that the presented approach could improve telemonitoring applications of patients with COPD by providing regular, unsupervised assessments of HR kinetics during daily-life routines. This could allow to early identify a decline in the patients' dynamic physiological functioning, physical fitness and/or health status.