title: Behavior-over-time graphs: assessing perceived trends in healthy eating and active living environments and behaviors across 49 communities.
abstract: BACKGROUND
  In the evaluation of the Healthy Kids, Healthy Communities initiative, investigators implemented Group Model Building (GMB) to promote systems thinking at the community level. As part of the GMB sessions held in each community partnership, participants created behavior-over-time graphs (BOTGs) to characterize their perceptions of changes over time related to policies, environments, collaborations, and social determinants in their community related to healthy eating, active living, and childhood obesity.
  
  
  OBJECTIVE
  To describe the process of coding BOTGs and their trends.
  
  
  METHODS
  Descriptive study of trends among BOTGs from 11 domains (eg, active living environments, social determinants of health, funding) and relevant categories and subcategories based on the graphed variables. In addition, BOTGs were distinguished by whether the variables were positively (eg, access to healthy foods) or negatively (eg, screen time) associated with health.
  
  
  METHODS
  The GMB sessions were held in 49 community partnerships across the United States.
  
  
  METHODS
  Participants in the GMB sessions (n = 590; n = 5-21 per session) included key individuals engaged in or impacted by the policy, system, or environmental changes occurring in the community.
  
  
  METHODS
  Thirty codes were developed to describe the direction (increasing, decreasing, stable) and shape (linear, reinforcing, balancing, or oscillating) of trends from 1660 graphs.
  
  
  RESULTS
  The patterns of trends varied by domain. For example, among variables positively associated with health, the prevalence of reinforcing increasing trends was highest for active living and healthy eating environments (37.4% and 29.3%, respectively), partnership and community capacity (38.8%), and policies (30.2%). Examination of trends of specific variables suggested both convergence (eg, for cost of healthy foods) and divergence (eg, for farmers' markets) of trends across partnerships.
  
  
  CONCLUSIONS
  Behavior-over-time graphs provide a unique data source for understanding community-level trends and, when combined with causal maps and computer modeling, can yield insights about prevention strategies to address childhood obesity.
authors.affiliation: Transtria LLC, St Louis, Missouri (Drs Hoehner and Brennan and Ms Kemner); Department of Systems Science and Industrial Engineering, State University of New York at Binghamton (Dr Sabounchi); and Social Systems Design Lab, Brown School of Social Work and Public Health, Washington University in St Louis, St Louis, Missouri (Dr Hovmand).
authors.forename: Christine M
authors.initials: CM
authors.lastname: Hoehner
authors.forename: Nasim S
authors.initials: NS
authors.lastname: Sabounchi
authors.forename: Laura K
authors.initials: LK
authors.lastname: Brennan
authors.forename: Peter
authors.initials: P
authors.lastname: Hovmand
authors.forename: Allison
authors.initials: A
authors.lastname: Kemner
mesh_terms: D000328:Adult; D002648:Child; D000072001:Diet, Healthy / Q000379:methods / Q000523:psychology* / Q000639:trends; D006293:Health Promotion / Q000379:methods; D006801:Humans; D009765:Obesity / Q000517:prevention & control / Q000523:psychology*; D010465:Perception; D011634:Public Health / Q000379:methods / Q000639:trends*; D012923:Social Class; D014481:United States