Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study

Association between greenery and health indicators in urban patients with symptomatic heart failure: a retrospective cohort study in Lithuania

Urban green spaces benefit physical, mental health, and reduses the risk of cardiovascular disease. A study in Kaunas, Lithuania collected health data from 100 patients with symptomatic heart failure (HF) during 2006-2009. Residential greenness was measured by the normalized difference vegetation index (NDVI). We assessed the impact of greenness on health indicators and on changes in health markers after 6 months. Higher greenness levels based on the NDVI 1-km radius were related to higher mean values of heart rate (HR) and ejection fraction and lower left ventricular (LV) end-diastolic diameter index (LV EDDI), LV end-systolic volume (ESV), left atrium size (LAS), and right atrium size (RAS) at baseline. After 6 months, a decrease in DBP and HR and an improvement in spiroergometric parameters were associated with exposure to high levels of greenness.  The long-term rehabilitation group experienced significant changes in spiroergometric indicators. The results confirm that the greenness of the residential environment can improve health indicators in patients with HF.

Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

PURPOSE OF REVIEW

Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing.

RECENT FINDINGS

Mental health research progressed in recent years towards integrating tools, such as smartphone based mental health assessments or mobile neuroimaging, allowing just-in-time daily assessments. Moreover, they are increasingly enriched by dynamic measurements of the environment, which are already being integrated with mental health assessments. To ensure ecological validity and accuracy it is crucial to capture environmental data with a high spatio-temporal granularity. Simultaneously, as a supplement to experimentally controlled conditions, there is a need for a better understanding of cognition in daily life, particularly regarding our brain's responses in natural settings.

SUMMARY

The presented overview on the developments and feasibility of "real-life" approaches for mental health and brain research and their potential to identify relationships along the mental health-environment-brain axis informs strategies for real-life individual and dynamic assessments.

Association between green space and multiple ambient air pollutants with depressive and anxiety symptoms among Chinese adolescents: The role of physical activity

OBJECTIVE

To explore the association between green space, multiple ambient air pollutants and depressive/anxiety symptoms and the mediating role of physical activity (PA) in Chinese adolescents.

METHOD

A school-based health survey was conducted in eight provinces in China in 2021. 22,868 students aged 14.64 (±1.77) years completed standard questionnaires to record details of depressive, anxiety symptoms and PA. We calculated the average normalized difference vegetation index (NDVI) in circular buffers of 200 m, 500 m and 1000 m and estimated the concentrations of PM10, PM2.5, CO, NO2, O3, SO2 around the adolescents' school addresses.

RESULTS

The exposure-response curves showed that the lower the NDVI value, the higher the risk of depressive and anxiety symptoms. CO, PM2.5 and SO2 and air pollution score were associated with increased risk of depressive and anxiety symptoms. NDVI in all circular buffers decreased the risk of depressive and anxiety symptoms at low levels of PA, but the associations were not significant at high levels of PA. In the subgroup analysis, PM10, PM2.5, CO, NO2, SO2, AQI and air pollution score increased the risk of depressive and anxiety symptoms at low PA levels, but the associations were not significant at high levels of PA. Mediation analysis indicated that the mediating effect of PA on the association between NDVI, NDVI-200 m NDVI-500 m, CO, PM10, PM2.5, SO2, AQI and depressive/anxiety symptoms was statistically significant（p < 0.05）.

CONCLUSION

Middle-high level PA could reduce the strength of association between air pollution and depressive and anxiety symptoms. Meanwhile, the association between green space/air pollution and depressive/anxiety symptoms was partly mediated by PA.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Mental health impacts of environmental exposures: A scoping review of evaluative instruments

To date, much of the health focus of environmental policy has been on preventing physical health impacts of environmental exposures. Recent research has however highlighted increasingly concurrent mental health effects and its consideration is an emerging requirement for many governments and their agencies, yet there are limited universal mental health assessment tools for environmental exposures. This paper details the findings of a scoping review that evaluated assessment tools used to measure psychological impacts from environmental exposures and pollution, as reported in recent peer-reviewed literature (2000-2022). Across the 126 papers identified in our review, a wide range of tools to assess mental health impact were identified. We document a clear recent upswing of research interest in the mental and psychological impacts of environmental exposures, and an overarching concern for air pollution from industry, traffic, and fires. A majority of studies utilised standardised assessment instruments, but there was little consistency in the way that these were combined or deployed. The dominant mental health outcomes of interest in these studies were depression, anxiety, and mental and psychiatric health. The findings of the review identify a need and opportunity to develop a best-practice approach to consistently assess the mental health impacts arising from environmental exposures. Future work is needed to define the most appropriate choice and application of assessment tools to evaluate adverse mental health impacts from environmental exposures. This will support a more universal, coordinated and cross-jurisdiction approach for the assessment, quantification and targeted response to addressing mental health impacts arising from environmental exposures.

Exposure to residential green and blue space and the natural environment is associated with a lower incidence of psychiatric disorders in middle-aged and older adults: findings from the UK Biobank

BACKGROUND

There is increasing evidence for the role of environmental factors and exposure to the natural environment on a wide range of health outcomes. Whether exposure to green space, blue space, and the natural environment (GBN) is associated with risk of psychiatric disorders in middle-aged and older adults has not been prospectively examined.

METHODS

Longitudinal data from the UK biobank was used. At the study baseline (2006-2010), 363,047 participants (women: 53.4%; mean age 56.7 ± 8.1 years) who had not been previously diagnosed with any psychiatric disorder were included. Follow-up was achieved by collecting records from hospitals and death registers. Measurements of green and blue space modeled from land use data and natural environment from Land Cover Map were assigned to the residential address for each participant. Cox proportional hazard models with adjustment for potential confounders were used to explore the longitudinal associations between GBN and any psychiatric disorder and then by specific psychiatric disorders (dementia, substance abuse, psychotic disorder, depression, and anxiety) in middle-aged and older adults.

RESULTS

During an average follow-up of 11.5 ± 2.8 years, 49,865 individuals were diagnosed with psychiatric disorders. Compared with the first tertile (lowest) of exposure, blue space at 300 m buffer [hazard ratio (HR): 0.973, 95% confidence interval (CI): 0.952-0.994] and natural environment at 300 m buffer (HR: 0.970, 95% CI: 0.948-0.992) and at 1000 m buffer (HR: 0.975, 95% CI: 0.952-0.999) in the third tertile (highest) were significantly associated with lower risk of incident psychiatric disorders, respectively. The risk of incident dementia was statistically decreased when exposed to the third tertile (highest) of green space and natural environment at 1000 m buffer. The third tertile (highest) of green space at 300 m and 1000 m buffer and natural environment at 300 m and 1000 m buffer was associated with a reduction of 30.0%, 31.8%, 21.7%, and 30.3% in the risk of developing a psychotic disorder, respectively. Subgroup analysis suggested that the elderly, men, and those living with some comorbid conditions may derive greater benefits associated with exposure to GBN.

CONCLUSIONS

This study suggests that GBN has significant benefits for lowering the risk of psychiatric disorders in middle-aged and older adults. Future studies are warranted to validate these findings and to understand the potential mechanistic pathways underpinning these novel findings.

Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay

BACKGROUND

Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.

OBJECTIVE

We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.

METHODS

We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space.

RESULTS

Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.

SIGNIFICANCE

For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

BACKGROUND

Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals' sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms.

METHODS

We cross-sectionally tracked 141 participants aged 18-65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents' anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms.

RESULTS

We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a "moderately health-threatening" exposure pattern were significantly associated with fewer anxiety symptoms than participants with a "strongly health-threatening" exposure pattern.

CONCLUSIONS

Our findings support that environmental exposures' daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.

Exposures to ambient air pollutants increase prevalence of sleep disorder in adults: Evidence from Wuhan Chronic Disease Cohort Study (WCDCS)

BACKGROUND

Sleep disorder contributes to memory dysfunction and chronic diseases. Clear evidence of environment disturbance, such as residential noise, are associated with an increased risk of sleep disorder. However, not enough studies have been conducted on association between residential air pollutants and sleep disorder. We sought to determine whether exposures to residential air pollutants associated with risk of sleep disorder among adults.

METHODS

Using the dataset of the Wuhan Chronic Disease Cohort Study (WCDCS), we investigated the prevalence of sleep disorder and five sleep disorder symptoms in the study. The data of air pollutants (including PM10, PM2.5, NO2, SO2 and O3) were obtained from 10 air quality monitoring stations in Wuhan. We utilized logistic regression model to evaluate the associations of five types of air pollutants with odds ratio (OR) of sleep disorder and symptoms. The potential moderating effects of socio-demographic factors in the associations were explored using the interaction effects model.

RESULTS

Of the study participants, 52.1 % had sleep disorder. Exposures to higher concentrations of air pollutants were associated with increased prevalence of sleep disorder. For example, per interquartile range (IQR) increases in concentrations of PM10, PM2.5 or SO2 corresponded to the increase of sleep disorder increased prevalence at 14.7 % (adjusted odds ratio (aOR) = 1.147, 95 %CI:1.062, 1.240), 8.9 % (aOR = 1.089, 95 %CI: 1.003, 1.182) and 15.8 % (aOR = 1.158, 95 %CI: 1.065, 1.260). For symptoms specific analyses, significant linkages of PM10, PM2.5, SO2 with difficulty in falling asleep, wake up after falling asleep and early awaken were observed. Moderating effects of age and place of residence on the linkages of PM10 with increased prevalence of sleep disorder were identified.

CONCLUSION

Higher level of air pollution exposure could increase the prevalence of sleep disorder. Middle-aged and elderly population, as well as the rural residents are more likely to suffer from sleep disorder.