Development of a dynamic framework to explain population patterns of leisure-time physical activity through agent-based modeling

Neighborhood factors associated with leisure-time physical activity in a Brazilian city

The purpose of this study was to estimate the association between environmental variables and leisure-time physical activity levels in an adult population of a medium-sized city in Brazil. This is a cross-sectional study with a sample of 965 adults (20–59 years). Individual, socioeconomic, demographic and behavioral data were obtained through a population-based survey. Environmental data were collected through direct observation and subsequently georeferenced. The outcome variable was leisure-time physical activity level, dichotomized as insufficiently active (<150 min/week) and active (≥150 min/week). Physical activity-related facilities were categorized as public or private, and food outlets as healthy, unhealthy or mixed. Monthly per capita income and crime rate by census tract were used to categorize the social environment. Statistical analysis was performed using binary logistic regression (generalized estimating equation models). A direct association was found between the availability of public (adjusted OR = 2.16; 95% CI = 1.44–3.25) and private (adjusted OR = 1.21; 95% CI = 1.02–1.44) physical activity facilities in the census tract and leisure-time physical activity. A greater availability of mixed food establishments was associated with higher leisure-time physical activity levels (adjusted OR = 1.10; 95% CI = 1.004–1.22). Environmental characteristics can contribute together to leisure-time physical activity and could be the focus of policies aimed at promoting physical activity in middle-income countries.

Using systems perspectives in evidence synthesis: A methodological mapping review

Reviewing complex interventions is challenging because they include many elements that can interact dynamically in a nonlinear manner. A systems perspective offers a way of thinking to help understand complex issues, but its application in evidence synthesis is not established. The aim of this project was to understand how and why systems perspectives have been applied in evidence synthesis. A methodological mapping review was conducted to identify papers using a systems perspective in evidence synthesis. A search was conducted in seven bibliographic databases and three search engines. A total of 101 papers (representing 98 reviews) met the eligibility criteria. Two categories of reviews were identified: (1) reviews using a "systems lens" to frame the topic, generate hypotheses, select studies, and guide the analysis and interpretation of findings (n = 76) and (2) reviews using systems methods to develop a systems model (n = 22). Several methods (e.g., systems dynamic modeling, soft systems approach) were identified, and they were used to identify, rank and select elements, analyze interactions, develop models, and forecast needs. The main reasons for using a systems perspective were to address complexity, view the problem as a whole, and understand the interrelationships between the elements. Several challenges for capturing the true nature and complexity of a problem were raised when performing these methods. This review is a useful starting point when designing evidence synthesis of complex interventions. It identifies different opportunities for applying a systems perspective in evidence synthesis, and highlights both commonplace and less familiar methods.

How can an agent-based model explore the impact of interventions on children's physical activity in an urban environment?

Insufficient physical activity (PA) among most children and adolescents is a global problem that is undermining the realisation of numerous developmental and health benefits. The aim of this study was to explore the potential impact of interventions on PA by using an agent-based model (ABM) simulating children's daily activities in an urban environment. Three domains for interventions were explored: outdoor play, school physical education and active travel. Simulated interventions increased children's average daily moderate-to-vigorous PA by 2–13 min and reduced the percentage of children not meeting PA guidelines, from 34% to 10%–29%, depending on the intervention. Promotion of active travel and outdoor play benefited more those in a higher socio-economic position. Agents' interactions suggested that: encouraging activity in diverse groups will reduce percentage of the least active in the population; and initiating outdoor events in neighbourhoods can generate an enhancing effect on children's engagement in PA. The ABM provided measurable outcomes for interventions that are difficult to estimate using reductionist methods. We suggest that ABMs should be used more commonly to explore the complexity of the social-environmental PA system.

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We developed an agent-based model simulating children's daily physical activity.

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Agents perform typical daily activities in a virtual urban model of Glasgow city.

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Simulations explore impact of interventions on outdoor play, school, active travel.

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Promotion of active travel had a differential impact across socio-economic position.

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Outdoor play intervention produced a non-linear increase in physical activity.

Physical Activity Promotion and the United Nations Sustainable Development Goals: Building Synergies to Maximize Impact

BACKGROUND

Many of the known solutions to the physical inactivity pandemic operate across sectors relevant to the United Nations Sustainable Development Goals (SDGs).

METHODS

The authors examined the contribution of physical activity promotion strategies toward achieving the SDGs through a conceptual linkage exercise, a scoping review, and an agent-based model.

RESULTS

Possible benefits of physical activity promotion were identified for 15 of the 17 SDGs, with more robust evidence supporting benefits for SDGs 3 (good health and well-being), 9 (industry, innovation, and infrastructure), 11 (sustainable cities and communities), 13 (climate action), and 16 (peace, justice, and strong institutions). Current evidence supports prioritizing at-scale physical activity-promoting transport and urban design strategies and community-based programs. Expected physical activity gains are greater for low-and middle-income countries. In high-income countries with high car dependency, physical activity promotion strategies may help reduce air pollution and traffic-related deaths, but shifts toward more active forms of travel and recreation, and climate change mitigation, may require complementary policies that disincentivize driving.

CONCLUSIONS

The authors call for a synergistic approach to physical activity promotion and SDG achievement, involving multiple sectors beyond health around their goals and values, using physical activity promotion as a lever for a healthier planet.

Is adiposity associated with objectively measured physical activity and sedentary behaviors in older adults?

BACKGROUND

Many older adults in the U.S. do not achieve the recommended amount of physical activity (PA) to fully realize a myriad of health benefits. Adiposity is one of those important correlates of PA and sedentary behaviors. However, the full extent to which adiposity is associated with PA and stationary time (STA) is uncertain. Therefore, we examined the association of adiposity with objectively measured PA and STA in black and white older adults.

METHODS

We conducted a cross-sectional study of older adults enrolled in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study 2003-2007 who participated in an ancillary accelerometer study 2009-2013. Assessment of body mass index (BMI) and waist circumference (WC) was completed during an in-home visit in the parent study. PA was measured by Actical™ accelerometers, which provided estimates of moderate-to-vigorous-intensity PA (MVPA), light-intensity PA (LPA), and STA for 4-7 consecutive days. Data from accelerometers were standardized to square root percentages of total wear time per day (SqrtMVPA%, SqrtLPA%, and SqrtSTA%). Interactions were tested for BMI and WC by race and sex, separately.

RESULTS

Data were available for 7873 participants (69.8 ± 8.7 yr, 54.2% women, 31.5% African American). In mixed linear regression models, significant interactions existed in BMI by race and sex for the SqrtMVPA%, WC by race and sex for the SqrtMVPA% and the SqrtLPA% model(p < 0.05). No interaction was significant for the logistic model of meeting the PA guideline or not. In subgroup analyses, BMI was inversely associated with SqrtMVPA%, SqrtLPA%, and positively related to SqrtSTA% in black women, white men and white women after adjustments. Similar patterns were observed between WC and SqrtMVPA%, SqrtLPA%, and SqrtSTA% in all groups, respectively. However, BMI was not associated with SqrtMVPA% in black men. Those with higher BMI or WC were less likely to meet the PA guideline in all groups.

CONCLUSIONS

Adiposity was inversely associated with higher levels of MVPA/LPA and positively associated with higher levels of STA among black and white older adults. Prevention efforts aimed at promoting weight control may be beneficial to prevent physical inactivity and sedentary lifestyle among older adults.

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.

Recommendations for Cardiovascular Health and Disease Surveillance for 2030 and Beyond: A Policy Statement From the American Heart Association

The release of the American Heart Association’s 2030 Impact Goal and associated metrics for success underscores the importance of cardiovascular health and cardiovascular disease surveillance systems for the acquisition of information sufficient to support implementation and evaluation. The aim of this policy statement is to review and comment on existing recommendations for and current approaches to cardiovascular surveillance, identify gaps, and formulate policy implications and pragmatic recommendations for transforming surveillance of cardiovascular disease and cardiovascular health in the United States. The development of community platforms coupled with widespread use of digital technologies, electronic health records, and mobile health has created new opportunities that could greatly modernize surveillance if coordinated in a pragmatic matter. However, technology and public health and scientific mandates must be merged into action. We describe the action and components necessary to create the cardiovascular health and cardiovascular disease surveillance system of the future, steps in development, and challenges that federal, state, and local governments will need to address. Development of robust policies and commitment to collaboration among professional organizations, community partners, and policy makers are critical to ultimately reduce the burden of cardiovascular disease and improve cardiovascular health and to evaluate whether national health goals are achieved.

Association of Social Networks and Physical Activity in South Asians: The Mediators of Atherosclerosis in South Asians Living in America Cohort Study

BACKGROUND

Eliminating racial/ethnic disparities in physical activity remains a challenge in the United States. South Asian immigrants in the United States have particularly low physical activity levels, and evidence suggests that social context may be important. This study examined associations between personal social networks and moderate to vigorous leisure-time physical activity (MVPA) among South Asians in the United States.

METHODS

We used cross-sectional data (2014-2017) from 689 South Asians (aged 43-85 y) who participated in the Mediators of Atherosclerosis in South Asians Living in America study. Self-reported physical activity and egocentric network data were collected from participants about their network members. Regression models were used to determine associations between social network characteristics and participants' MVPA.

RESULTS

Participants were on average 59 years old (SD = 9) and reported 1335 metabolic equivalent minutes per week of MVPA (interquartile range = 735, 2212). Having network members who exercised or who were exercise partners associated with increased MVPA in men (β coefficient = 241 MET min/wk [95% confidence interval, 63 to 419] and β = 520 MET min/wk [95% confidence interval, 322 to 718], respectively). For women, the association was only significant if the exercise partner was a spouse.

CONCLUSION

Physical activity interventions utilizing network members as exercise partners may have potential in South Asians but must consider gender differences.

Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC

Background

Decreasing sedentary behaviour (SB) has emerged as a public health priority since prolonged sitting increases the risk of non-communicable diseases. Mostly, the independent association of factors with SB has been investigated, although lifestyle behaviours are conditioned by interdependent factors. Within the DEDIPAC Knowledge Hub, a system of sedentary behaviours (SOS)-framework was created to take interdependency among multiple factors into account. The SOS framework is based on a system approach and was developed by combining evidence synthesis and expert consensus. The present study conducted a Bayesian network analysis to investigate and map the interdependencies between factors associated with SB through the life-course from large scale empirical data.

Methods

Data from the Eurobarometer survey (80.2, 2013) that included the International physical activity questionnaire (IPAQ) short as well as socio-demographic information and questions on perceived environment, health, and psychosocial information were enriched with macro-level data from the Eurostat database. Overall, 33 factors were identified aligned to the SOS-framework to represent six clusters on the individual or regional level: 1) physical health and wellbeing, 2) social and cultural context, 3) built and natural environment, 4) psychology and behaviour, 5) institutional and home settings, 6) policy and economics. A Bayesian network analysis was conducted to investigate conditional associations among all factors and to determine their importance within these networks. Bayesian networks were estimated for the complete (23,865 EU-citizens with complete data) sample and for sex- and four age-specific subgroups. Distance and centrality were calculated to determine importance of factors within each network around SB.

Results

In the young (15–25), adult (26–44), and middle-aged (45–64) groups occupational level was directly associated with SB for both, men and women. Consistently, social class and educational level were indirectly associated within male adult groups, while in women factors of the family context were indirectly associated with SB. Only in older adults, factors of the built environment were relevant with regard to SB, while factors of the home and institutional settings were less important compared to younger age groups.

Conclusion

Factors of the home and institutional settings as well as the social and cultural context were found to be important in the network of associations around SB supporting the priority for future research in these clusters. Particularly, occupational status was found to be the main driver of SB through the life-course. Investigating conditional associations by Bayesian networks gave a better understanding of the complex interplay of factors being associated with SB. This may provide detailed insights in the mechanisms behind the burden of SB to effectively inform policy makers for detailed intervention planning. However, considering the complexity of the issue, there is need for a more comprehensive system of data collection including objective measures of sedentary time.

A multivalued agent-based model for the study of noncommunicable diseases

This paper aims to test and illustrate the utility and extensibility of an existing model, SimNCD (Simulation of NonCommunicable Diseases). It also proposes a way to include questionnaires - widely used in epidemiology - in the individual's reasoning mechanism in order to identify his/her attitude and personal choices. SimNCD is a formal agent-based model. It helps researchers and health practitioners study and simulate the complex dynamics of noncommunicable diseases. It models individuals that evolve within a social network, and behave while engaging in activities offered by their physical environment. The literature strongly supports the influence of the individual's behavioral choices on their health, particularly, the acquirement and maintainability of noncommunicable diseases. Therefore, we propose to extend SimNCD in order to acquire the agents with a reasoning process that allows them to choose the activities to practice. Thus, we model their attitude via preferences that are modeled based on the available literature and expressed with the linguistic 2-tuple method. Our solution also employs a multi-attribute decision-making method. We specify the proposed solution in the study of childhood obesity and use it to predict children's corpulence variations in different scenarios.

Exploring the emergence and evolution of population patterns of leisure-time physical activity through agent-based modelling

INTRODUCTION

Most interventions aiming to promote leisure-time physical activity (LTPA) at population level showed small or null effects. Approaching the problem from a systems science perspective may shed light on the reasons for these results. We developed an agent-based model to explore how the interactions between psychological attributes and built and social environments may lead to the emergence and evolution of LTPA patterns among adults.

METHODS

The modeling process consisted of four stages: (1) conceptual model development, (2) formulation of the agent-based model, (3) parametrization and calibration, and (4) consistency and sensitivity analyses. The model represents a stylized community containing two types of agents: persons and LTPA sites. Persons interact with each other (proximal network and perceived community) and with the built environment (LTPA sites) over time. Decision-making is based on the person's intention to practice LTPA, conditioned to the perceived environment. Each iteration is equivalent to one week and we assessed a period of 10 years.

RESULTS

The model was able to reproduce population temporal trends of intention and LTPA reported in the literature. Sensitivity analyses indicated that population patterns and trends of intention and LTPA were highly influenced by the relationship between a person's behavior in the preceding week and his current intention, the person's access to built and social environment, and the density of LTPA sites.

CONCLUSIONS

The proposed agent-based model is suitable to explore the emergence and evolution of LTPA patterns among adults, considering the dynamic interaction between individuals' psychological attributes and the built and social environments in which they live. The model is available at https://doi.org/10.17605/OSF.IO/J2KAS .

How has big data contributed to obesity research? A review of the literature

There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.