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Economos CD, Calancie L, Korn AR, Allender S, Appel JM, Bakun P, Hennessy E, Hovmand PS, Kasman M, Nichols M, Pachucki MC, Swinburn BA, Tovar A, Hammond RA. Community coalition efforts to prevent childhood obesity: two-year results of the Shape Up Under 5 study. BMC Public Health 2023; 23:529. [PMID: 36941543 PMCID: PMC10026415 DOI: 10.1186/s12889-023-15288-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 02/17/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Cross-sector collaborations and coalitions are promising approaches for childhood obesity prevention, yet there is little empirical evidence about how they affect change. We hypothesized that changes in knowledge of, and engagement with, childhood obesity prevention among coalition members can diffuse through social networks to influence policies, systems, and environments. METHODS We studied a community coalition (N = 16, Shape Up Under 5 "SUU5 Committee") focused on early childhood obesity prevention in Somerville, MA from 2015-17. Knowledge, engagement, and social network data were collected from Committee members and their network contacts (n = 193) at five timepoints over two years. Policy, systems, and environment data were collected from the SUU5 Committee. Data were collected via the validated COMPACT Stakeholder-driven Community Diffusion survey and analyzed using regression models and social network analysis. RESULTS Over 2 years, knowledge of (p = 0.0002), and engagement with (p = 0.03), childhood obesity prevention increased significantly among the SUU5 Committee. Knowledge increased among the Committee's social network (p = 0.001). Significant changes in policies, systems, and environments that support childhood obesity prevention were seen from baseline to 24 months (p = 0.003). CONCLUSION SUU5 had positive effects on "upstream" drivers of early childhood obesity by increasing knowledge and engagement. These changes partially diffused through networks and may have changed "midstream" community policies, systems, and environments.
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Affiliation(s)
- Christina D Economos
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA.
| | - Larissa Calancie
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA
| | - Ariella R Korn
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA
| | - Steven Allender
- Institute for Health Transformation, Deakin University, Geelong, Australia
| | - Julia M Appel
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA
| | - Peter Bakun
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA
| | - Erin Hennessy
- Friedman School of Nutrition Science and Policy, Tufts University, Tufts University, 150 Harrison Ave., Boston, MA, 02111, USA
| | - Peter S Hovmand
- Center for Community Health Integration, Case Western Reserve University, Cleveland, OH, USA
| | - Matt Kasman
- Economic Studies, Brookings, Washington, D.C., USA
| | - Melanie Nichols
- Institute for Health Transformation, Deakin University, Geelong, Australia
| | - Mark C Pachucki
- Sociology and Computational Social Science Institute, University of Massachusetts, Amherst, MA, USA
| | - Boyd A Swinburn
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Alison Tovar
- Behavioral and Social Sciences, Brown University, Providence, RI, USA
| | - Ross A Hammond
- Economic Studies, Brookings, Washington, D.C., USA
- Brown School, Washington University in St Louis, St Louis, MO, USA
- Santa Fe Institute, Santa Fe, NM, USA
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A mixed methods analysis of environmental and household chaos: considerations for early-childhood obesity research. BMC Public Health 2021; 21:1867. [PMID: 34654393 PMCID: PMC8520198 DOI: 10.1186/s12889-021-11936-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Chaos has implications for child health that may extend to childhood obesity. Yet, results from studies describing associations between chaos and childhood obesity are mixed. New approaches to studying the environments of young children may help to clarify chaos-obesity relationships. METHODS We conducted a concurrent mixed methods analysis of quantitative and qualitative data describing home and neighborhood chaos among a diverse cohort of 283 caregiver-toddlers dyads from Ohio. We examined the underlying structure of environmental and household chaos using exploratory factor analysis then sought to validate the structure using qualitative field notes. We generated total scores for factors of chaos and described their distributions overall and according to cohort characteristics. Additionally, we conducted a thematic content analysis of brief ethnographies to provide preliminary construct validity for our indicators of chaos. RESULTS Dyads varied according to household composition, income, education, and race/ethnicity. We found evidence for a multi-factor structure for chaos, which included disorganization and neighborhood noise. Household disorganization scores ranged from 0 to 7.3 and were on average 2.1 (SD = 1.8). Neighborhood noise scores ranged from 0 to 4 and were on average 1.1 (SD = 1.1). Both disorganization and neighborhood noise were associated with indicators of socioeconomic disadvantage, such as lower educational attainment and household income. Qualitative data from households with high and low scores on the two identified factors were aligned in ways that were supportive of construct validity and further contextualized the social and material environments in which chaos occurred. CONCLUSIONS Chaos represents a complex construct with implications spanning various disciplines, including childhood obesity research. Previous studies suggest challenges associated with measuring chaos may limit the conclusions that can be drawn about which aspect of chaos (if any) matter most of early childhood weight development. We advance the literature by demonstrating chaos may be comprised of conceptually distinct subdomains. Future childhood obesity prevention research may benefit from more contemporary measure of chaos, such as those relying on direct observations that account for a multifaceted underlying structure.
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