The SPOTLIGHT virtual audit tool: a valid and reliable tool to assess obesogenic characteristics of the built environment.

BACKGROUND: A lack of physical activity and overconsumption of energy dense food is associated with overweight and obesity. The neighbourhood environment may stimulate or hinder the development and/or maintenance of a healthy lifestyle. To improve research on the obesogenicity of neighbourhood environments, reliable, valid and convenient assessment methods of potential obesogenic characteristics of neighbourhood environments are needed. This study examines the reliability and validity of the SPOTLIGHT-Virtual Audit Tool (S-VAT), which uses remote sensing techniques (Street View feature in Google Earth) for desk-based assessment of environmental obesogenicity. METHODS: A total of 128 street segments in four Dutch urban neighbourhoods - heterogeneous in socio-economic status and residential density - were assessed using the S-VAT. Environmental characteristics were categorised as walking related items, cycling related items, public transport, aesthetics, land use-mix, grocery stores, food outlets and physical activity facilities. To assess concordance of inter- and intra-observer reliability of the Street View feature in Google Earth, and validity scores with real life audits, percentage agreement and Cohen's Kappa (k) were calculated. RESULTS: Intra-observer reliability was high and ranged from 91.7% agreement (k = 0.654) to 100% agreement (k = 1.000) with an overall agreement of 96.4% (k = 0.848). Inter-observer reliability results ranged from substantial agreement 78.6% (k = 0.440) to high agreement, 99.2% (k = 0.579), with an overall agreement of 91.5% (k = 0.595). Criterion validity was substantial to high for most of the categories ranging from 87.3% agreement (k = 0.539) to 99.9% agreement (k = 0.887) with an overall score of 95.6% agreement (k = 0.747). CONCLUSION: These study results suggest that the S-VAT is a highly reliable and valid remote sensing tool to assess potential obesogenic environmental characteristics

Neighbourhood drivability: environmental and individual characteristics associated with car use across Europe

Background: Car driving is a form of passive transportation associated with higher sedentary behaviour, which is associated with morbidity. The decision to drive a car is likely to be influenced by the ‘drivability’ of the built environment, but there is lack of scientific evidence regarding the relative contribution of environmental characteristics of car driving in Europe, compared to individual characteristics. This study aimed to determine which neighbourhood- and individual-level characteristics were associated with car driving in adults of five urban areas across Europe. Second, the study aimed to determine the percentage of variance in car driving explained by individual- and neighbourhood-level characteristics. Methods: Neighbourhood environment characteristics potentially related to car use were identified from the literature. These characteristics were subsequently assessed using a Google Street View audit and available GIS databases, in 59 administrative residential neighbourhoods in five European urban areas. Car driving (min/week) and individual level characteristics were self-reported by study participants (analytic sample n = 4258). We used linear multilevel regression analyses to assess cross-sectional associations of individual and neighbourhood-level characteristics with weekly minutes of car driving, and assessed explained variance at each level and for the total model. Results: Higher residential density (β:-2.61, 95%CI: − 4.99; -0.22) and higher land-use mix (β:-3.73, 95%CI: − 5.61; -1.86) were significantly associated with fewer weekly minutes of car driving. At the individual level, higher age (β: 1.47, 95%CI: 0.60; 2.33), male sex (β: 43.2, 95%CI:24.7; 61.7), being employed (β:80.1, 95%CI: 53.6; 106.5) and ≥ 3 person household composition (β: 47.4, 95%CI: 20.6; 74.2) were associated with higher weekly minutes of car driving. Individual and neighbourhood characteristics contributed about equally to explained variance in minutes of weekly car driving, with 2 and 3% respectively, but total explained variance remained low. Conclusions: Residential density and land-use mix were neighbourhood characteristics consistently associated with minutes of weekly car driving, besides age, sex, employment and household composition. Although total explained variance was low, both individual- and neighbourhood-level characteristics were similarly important in their associations with car use in five European urban areas. This study suggests that more, higher quality, and longitudinal data are needed to increase our understanding of car use and its effects on determinants of health

Variation in population levels of physical activity in European adults according to cross-European studies: a systematic literature review within DEDIPAC

peer-reviewedBackground: Physical inactivity is a well-known public health risk that should be monitored at the population level. Physical activity levels are often surveyed across Europe. This systematic literature review aims to provide an overview of all existing cross-European studies that assess physical activity in European adults, describe the variation in population levels according to these studies, and discuss the impact of the assessment methods. Methods: Six literature databases (PubMed, EMBASE, CINAHL, PsycINFO, SportDiscus and OpenGrey) were searched, supplemented with backward- and forward tracking and searching authors’ and experts’ literature databases. Articles were included if they reported on observational studies measuring total physical activity and/or physical activity in leisure time in the general population in two or more European countries. Each record was reviewed, extracted and assessed by two independent researchers and disagreements were resolved by a third researcher. The review protocol of this review is registered in the PROSPERO database under registration number CRD42014010334. Results: Of the 9,756 unique identified articles, twenty-five were included in this review, reporting on sixteen different studies, including 2 to 35 countries and 321 to 274,740 participants. All but two of the studies used questionnaires to assess physical activity, with the majority of studies using the IPAQ-short questionnaire. The remaining studies used accelerometers. The percentage of participants who either were or were not meeting the physical activity recommendations was the most commonly reported outcome variable, with the percentage of participants meeting the recommendations ranging from 7 % to 96 % across studies and countries. Conclusions: The included studies showed substantial variation in the assessment methods, reported outcome variables and, consequently, the presented physical activity levels. Because of this, absolute population levels of physical activity in European adults are currently unknown. However, when ranking countries, Ireland, Italy, Malta, Portugal, and Spain generally appear to be among the less active countries. Objective data of adults across Europe is currently limited. These findings highlight the need for standardisation of the measurement methods, as well as cross-European monitoring of physical activity levels

A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach

Background: Recent research shows that sedentary behaviour is associated with adverse cardio-metabolic consequences even among those considered sufficiently physically active. In order to successfully develop interventions to address this unhealthy behaviour, factors that influence sedentariness need to be identified and fully understood. The aim of this review is to identify individual, social, environmental, and policy-related determinants or correlates of sedentary behaviours among adults aged 18-65 years. Methods: PubMed, Embase, CINAHL, PsycINFO and Web of Science were searched for articles published between January 2000 and September 2015. The search strategy was based on four key elements and their synonyms: (a) sedentary behaviour (b) correlates (c) types of sedentary behaviours (d) types of correlates. Articles were included if information relating to sedentary behaviour in adults (18-65 years) was reported. Studies on samples selected by disease were excluded. The full protocol is available from PROSPERO (PROSPERO 2014:CRD42014009823). Results: 74 original studies were identified out of 4041: 71 observational, two qualitative and one experimental study. Sedentary behaviour was primarily measured as self-reported screen leisure time and total sitting time. In 15 studies, objectively measured total sedentary time was reported: accelerometry (n = 14) and heart rate (n = 1). Individual level factors such as age, physical activity levels, body mass index, socio-economic status and mood were all significantly correlated with sedentariness. A trend towards increased amounts of leisure screen time was identified in those married or cohabiting while having children resulted in less total sitting time. Several environmental correlates were identified including proximity of green space, neighbourhood walkability and safety and weather. Conclusions: Results provide further evidence relating to several already recognised individual level factors and preliminary evidence relating to social and environmental factors that should be further investigated. Most studies relied upon cross-sectional design limiting causal inference and the heterogeneity of the sedentary measures prevented direct comparison of findings. Future research necessitates longitudinal study designs, exploration of policy-related factors, further exploration of environmental factors, analysis of inter-relationships between identified factors and better classification of sedentary behaviour domains

Positive and negative well-being and objectively measured sedentary behaviour in older adults: evidence from three cohorts

Background: Sedentary behaviour is related to poorer health independently of time spent in moderate to vigorous physical activity. The aim of this study was to investigate whether wellbeing or symptoms of anxiety or depression predict sedentary behaviour in older adults. Method: Participants were drawn from the Lothian Birth Cohort 1936 (LBC1936) (n = 271), and the West of Scotland Twenty-07 1950s (n = 309) and 1930s (n = 118) cohorts. Sedentary outcomes, sedentary time, and number of sit-to-stand transitions, were measured with a three-dimensional accelerometer (activPAL activity monitor) worn for 7 days. In the Twenty-07 cohorts, symptoms of anxiety and depression were assessed in 2008 and sedentary outcomes were assessed ~ 8 years later in 2015 and 2016. In the LBC1936 cohort, wellbeing and symptoms of anxiety and depression were assessed concurrently with sedentary behaviour in 2015 and 2016. We tested for an association between wellbeing, anxiety or depression and the sedentary outcomes using multivariate regression analysis. Results: We observed no association between wellbeing or symptoms of anxiety and the sedentary outcomes. Symptoms of depression were positively associated with sedentary time in the LBC1936 and Twenty-07 1950s cohort, and negatively associated with number of sit-to-stand transitions in the LBC1936. Meta-analytic estimates of the association between depressive symptoms and sedentary time or number of sit-to-stand transitions, adjusted for age, sex, BMI, long-standing illness, and education, were β = 0.11 (95% CI = 0.03, 0.18) and β = − 0.11 (95% CI = − 0.19, −0.03) respectively. Conclusion: Our findings indicate that depressive symptoms are positively associated with sedentary behavior. Future studies should investigate the causal direction of this association

Primary prevention of diabetes mellitus type 2 and cardiovascular diseases using a cognitive behavior program aimed at lifestyle changes in people at risk: Design of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>The number of people with cardiovascular disease (CVD) and diabetes mellitus type 2 (T2DM) is growing rapidly. To a large extend, this increase is due to lifestyle-dependent risk factors, such as overweight, reduced physical activity, and an unhealthy diet. Changing these risk factors has the potential to postpone or prevent the development of T2DM and CVD. It is hypothesized that a cognitive behavioral program (CBP), focused in particular on motivation and self-management in persons who are at high risk for CVD and/or T2DM, will improve their lifestyle behavior and, as a result, will reduce their risk of developing T2DM and CVD.</p> <p>Methods</p> <p>12,000 inhabitants, 30-50 years of age living in several municipalities in the semi-rural region of West-Friesland will receive an invitation from their general practitioner (n = 13) to measure their own waist circumference with a tape measure. People with abdominal obesity (male waist ≥ 102 cm, female waist ≥ 88 cm) will be invited to participate in the second step of the screening which includes blood pressure, a blood sample and anthropometric measurements. T2DM and CVD risk scores will then be calculated according to the ARIC and the SCORE formulae, respectively. People with a score that indicates a high risk of developing T2DM and/or CVD will then be randomly assigned to the intervention group (n = 300) or the control group (n = 300).</p> <p>Participants in the intervention group will follow a CBP aimed at modifying their dietary behavior, physical activity, and smoking behavior. The counseling methods that will be used are <it>motivational interviewing </it>(MI) and <it>problem solving treatment </it>(PST), which focus in particular on intrinsic motivation for change and self-management of problems of the participants. The CBP will be provided by trained nurse practitioners in the participant's general practice, and will consists of a maximum of six individual sessions of 30 minutes, followed by 3-monthly booster sessions by phone. Participants in the control group will receive brochures containing health guidelines regarding physical activity and diet, and how to stop smoking. The primary outcome measures will be changes in T2DM and CVD risk scores. Secondary outcome measures will be changes in lifestyle behavior and cost-effectiveness and cost-utility ratios. All relevant direct and indirect costs will be measured, and there will be a follow-up of 24 months.</p> <p>Discussion</p> <p>Changing behaviors is difficult, requires time, considerable effort and motivation. Combining the two counseling methods MI and PST, followed by booster sessions may result in sustained behavioral change.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN59358434</p

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in formula presented buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to formula presented . The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.</p

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in formula presented buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to formula presented . The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.</p