Associations between neighbourhood characteristics, physical activity and depressive symptoms: the Northern Finland Birth Cohort 1966 Study.

BACKGROUND: Due to rapid urbanization, there is a need to better understand the relative roles of residential environment and physical activity in depression. We aimed to investigate whether neighbourhood characteristics are related to the presence of depressive symptoms and whether the association is modified by physical activity. METHODS: This cross-sectional study used the 46-year-old follow-up data (n = 5489) from the Northern Finland Birth Cohort 1966. Data on depressive symptoms, measured by Beck Depression Inventory-II, and self-reported and accelerometer-measured physical activity were included. Neighbourhood characteristics, population density, distance to the closest grocery store, bus stops and cycle/pedestrian paths, distance to the nearest parks and forests, residential greenness and level of urbanicity were calculated using Geographic Information System methods based on participants' home coordinates. RESULTS: According to ordinal logistic regression analyses adjusted for physical activity at different intensities and individual covariates, living in a neighbourhood with higher population density and urbanicity level were associated with a higher risk of experiencing more severe depressive symptoms. Higher residential greenness was associated with a lower risk of experiencing more severe depressive symptoms after adjustment for self-reported light and moderate-to-vigorous physical activity, accelerometer-measured moderate-to-vigorous physical activity and individual covariates. Both higher self-reported and accelerometer-measured physical activity were independently associated with a lower risk of more severe depressive symptoms. CONCLUSIONS: Both residential environment and physical activity behaviour play an important role in depressive symptoms; however, further research among populations of different ages is required. Our findings can be utilized when designing interventions for the prevention of depression.

Residential history and changes in perceived health-The Northern Finland Birth Cohort 1966 study.

Neighbourhood socioeconomic status and physical access to amenities and greenness are factors that have been associated with mental, physical and perceived health. However, associations between long-time exposure to these circumstances and changes in perceived health in the middle-age population have remained a relatively underexamined area. This study aimed to examine the association between residential environmental history and changes in perceived health in the Northern Finland Birth Cohort 1966 (N = 5973) population encompassing the two latest data collections at 31 and 46 years of age. Longitudinal time-varying geographical data on the residential environment's economic dependency ratio, population density, distance to local services and presence of green areas were derived from various spatial registers and linked to cohort members' exact residential history. According to a multivariable logistic regression analysis, having a residential history in municipalities with higher-than-average (poor) economic dependency ratios was associated with higher odds of poor perceived health changing to good. Among men, living farther than 2 km away from local services was associated with a higher risk of change from good perceived health to poor, and living farther than 300 m away from green areas was associated with a lower risk of change from good perceived health to poor. The residential environments's urban/rural context may be one factor contributing to the findings. The results point to the importance of considering local residential area characteristics and residence duration in certain areas as potential determinants of health. Finally, having long-term residential history in areas with poor access to services and amenities has the potential to undermine health during one's lifetime.

Satellite Imaging-Based Residential Greenness and Accelerometry Measured Physical Activity at Midlife-Population-Based Northern Finland Birth Cohort 1966 Study.

BACKGROUND: Recently, the importance of light physical activity (LPA) for health has been emphasized, and residential greenness has been positively linked to the level of LPA and a variety of positive health outcomes. However, people spend less time in green environments because of urbanization and modern sedentary leisure activities. AIMS: In this population-based study, we investigated the association between objectively measured residential greenness and accelerometry measured physical activity (PA), with a special interest in LPA and gender differences. METHODS: The study was based on the Northern Finland Birth Cohort 1966 (5433 members). Participants filled in a postal questionnaire and underwent clinical examinations and wore a continuous measurement of PA with wrist-worn Polar Active Activity Monitor accelerometers for two weeks. The volume of PA (metabolic equivalent of task or MET) was used to describe the participant's total daily activity (light: 2-3.49 MET; moderate: 3.5-4.99 MET; vigorous: 5-7.99 MET; very vigorous: ≥8 MET). A geographic information system (GIS) was used to assess the features of each individual's residential environment. The normalized difference vegetation index (NDVI) was used for the objective quantification of residential greenness. Multiple linear regression and a generalized additive model (GAM) were used to analyze the association between residential greenness and the amount of PA at different intensity levels. RESULTS: Residential greenness (NDVI) was independently associated with LPA (unadjusted ß = 174; CI = 140, 209) and moderate physical activity (MPA) (unadjusted ß = 75; CI = 48, 101). In the adjusted model, residential greenness was positively and significantly associated with LPA (adjusted ß = 70; CI = 26, 114). In men, residential greenness was positively and significantly associated with LPA (unadjusted ß = 224; CI = 173, 275), MPA (unadjusted ß = 75; CI = 48, 101), and moderate to vigorous physical activity (MVPA) (unadjusted ß = 89; CI = 25, 152). In women, residential greenness was positively related to LPA (unadjusted ß = 142; CI = 96, 188) and inversely associated with MPA (unadjusted ß = -22; CI = -36, -8), vigorous/very vigorous physical activity (VPA/VVPA) (unadjusted ß = -49; CI = -84, -14), and MVPA (unadjusted ß = -71; CI = -113, -29). In the final adjusted models, residential greenness was significantly associated only with the amount of LPA in men (adjusted ß = 140; CI = 75, 204). CONCLUSIONS: Residential greenness was positively associated with LPA in both genders, but the association remained significant after adjustments only in men. Residential greenness may provide a supportive environment for promoting LPA.

Correlates of physical activity behavior in adults: a data mining approach.

PURPOSE: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. METHODS: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. RESULTS: Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = - 16.1, and MVPA: B = - 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = - 3.7). CONCLUSIONS: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

Residential relocation trajectories and neighborhood density, mixed land use and access networks as predictors of walking and bicycling in the Northern Finland Birth Cohort 1966.

BACKGROUND: Given the high global prevalence of physical inactivity, there is a need to design cities that support active modes of transportation. High density diverse neighborhoods with good access networks have been associated with enhanced walking and cycling, but there is a lack of large-scale longitudinal studies utilizing a life course perspective to model residential relocation trajectories. The objectives of the present longitudinal study were to model and visualize residential relocation trajectories between 31 and 46 years of age based on neighborhood density, mixed land use and access networks (DMA), and to assess neighborhood DMA as a predictor of self-reported regular walking and cycling and objectively measured physical activity. METHODS: Based on data from the Northern Finland Birth Cohort 1966 (N = 5947), we used self-reported regular walking and cycling and objectively measured physical activity as outcome variables and objectively assessed neighborhood DMA as the main explanatory variable. We conducted sequence analysis to model residential relocation trajectories, and generalized linear mixed models and Fisher's exact test were used to explore longitudinal associations between neighborhood DMA and physical activity. RESULTS: Over 80% of the participants lived in a neighborhood with the same level of neighborhood DMA during the follow-up. Relocation occurred more often from higher to lower DMA neighborhoods than reverse. Increased neighborhood DMA was associated with increased regular walking (OR 1.03; 95% CI: 1.00, 1.05; p = 0.023) and cycling (OR 1.17; 95% CI: 1.12, 1.23; p <  0.001). Residential relocation trajectory from lower to highest neighborhood DMA increased the odds of starting regular walking (OR 3.15; 95% CI: 1.50, 7.14; p = 0.001) and cycling (OR 2.63; 95% CI: 1.23, 5.79; p = 0.009) as compared to higher to lower neighborhood DMA trajectory. CONCLUSIONS: The results strongly support the hypothesis that increasing urban DMA can enhance regular walking and cycling at population level and so improve public health. The findings have implications for zoning and transportation policies, favoring the creation of dense and diverse neighborhoods with good access networks to support regular walking and cycling.

The Built Environment as a Determinant of Physical Activity: A Systematic Review of Longitudinal Studies and Natural Experiments.

Background: Physical inactivity is a global problem that increases the risk of many chronic diseases and shortens life expectancy. The built environment contributes to physical inactivity through accessibility of amenities and transportation patterns. With better urban planning, cities could be designed to enhance active transportation and population health on a permanent basis. Purpose: We conducted a systematic review to identify determinants of the built environment associated with physical activity and to evaluate how changes in the built environment are associated with changes in physical activity. Methods: We searched six databases, from the inception of each until December 2015, for studies that were written in English, used longitudinal before-and-after design and assessed changes in both the built environment and physical activity. A total of 21 prospective cohort studies and 30 natural experiments were included in the review. Results: The review showed that changes in the built environment and in physical activity were related. A higher objective accessibility and new infrastructure for walking, cycling and public transportation were associated with increased overall and transportation-related physical activity. Some evidence was found for perceived aesthetics and safety as determinants of physical activity. Conclusions: Improved objectively measured accessibility of different type of destinations and public transportation and land use mix were associated with increased physical activity. Creating new infrastructure for walking, cycling and public transportation could induce demand for walking and cycling. The results support the creation of compact and diverse residential areas and investments into infrastructure that encourage active modes of transportation.