UNLABELLED: Action Learning Collaboratives (ALCs), whereby teams apply quality improvement (QI) tools and methods, have successfully improved patient care delivery and outcomes. We adapted and tested the ALC model as a community-based obesity prevention intervention focused on physical activity and healthy eating. METHOD: The intervention used QI tools (e.g., progress monitoring) and team-based activities and was implemented in three communities through nine monthly meetings. To assess process and outcomes, we used a longitudinal repeated-measures and mixed-methods triangulation approach with a quasi-experimental design including objective measures at three time points. RESULTS: Most of the 97 participants were female (85.4%), White (93.8%), and non-Hispanic/Latino (95.9%). Average age was 52 years; 28.0% had annual household income of $20,000 or less; and mean body mass index was 35. Through mixed-effects models, we found some physical activity outcomes improved. Other outcomes did not significantly change. Although participants favorably viewed the QI tools, components of the QI process such as sharing goals and data on progress in teams and during meetings were limited. Participants' requests for more education or activities around physical activity and healthy eating, rather than progress monitoring and data sharing required for QI activities, challenged ALC model implementation. CONCLUSIONS: An ALC model for community-based obesity prevention may be more effective when applied to preexisting teams in community-based organizations.
UNLABELLED: Action Learning Collaboratives (ALCs), whereby teams apply quality improvement (QI) tools and methods, have successfully improved patient care delivery and outcomes. We adapted and tested the ALC model as a community-based obesity prevention intervention focused on physical activity and healthy eating. METHOD: The intervention used QI tools (e.g., progress monitoring) and team-based activities and was implemented in three communities through nine monthly meetings. To assess process and outcomes, we used a longitudinal repeated-measures and mixed-methods triangulation approach with a quasi-experimental design including objective measures at three time points. RESULTS: Most of the 97 participants were female (85.4%), White (93.8%), and non-Hispanic/Latino (95.9%). Average age was 52 years; 28.0% had annual household income of $20,000 or less; and mean body mass index was 35. Through mixed-effects models, we found some physical activity outcomes improved. Other outcomes did not significantly change. Although participants favorably viewed the QI tools, components of the QI process such as sharing goals and data on progress in teams and during meetings were limited. Participants' requests for more education or activities around physical activity and healthy eating, rather than progress monitoring and data sharing required for QI activities, challenged ALC model implementation. CONCLUSIONS: An ALC model for community-based obesity prevention may be more effective when applied to preexisting teams in community-based organizations.
Authors: Danice K Eaton; Laura Kann; Steve Kinchen; Shari Shanklin; Katherine H Flint; Joseph Hawkins; William A Harris; Richard Lowry; Tim McManus; David Chyen; Lisa Whittle; Connie Lim; Howell Wechsler Journal: MMWR Surveill Summ Date: 2012-06-08
Authors: Rebecca S Mozaffarian; Jean L Wiecha; Barbara A Roth; Toben F Nelson; Rebekka M Lee; Steven L Gortmaker Journal: Am J Public Health Date: 2009-10-15 Impact factor: 9.308
Authors: G T O'Connor; S K Plume; E M Olmstead; J R Morton; C T Maloney; W C Nugent; F Hernandez; R Clough; B J Leavitt; L H Coffin; C A Marrin; D Wennberg; J D Birkmeyer; D C Charlesworth; D J Malenka; H B Quinton; J F Kasper Journal: JAMA Date: 1996-03-20 Impact factor: 56.272
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