11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Translating a child care based intervention for online delivery: development and randomized pilot study of Go NAPSACC

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          As part of childhood obesity prevention initiatives, Early Care and Education (ECE) programs are being asked to implement evidence-based strategies that promote healthier eating and physical activity habits in children. Translation of evidence-based interventions into real world ECE settings often encounter barriers, including time constraints, lack of easy-to-use tools, and inflexible intervention content. This study describes translation of an evidence-based program (NAPSACC) into an online format (Go NAPSACC) and a randomized pilot study evaluating its impact on centers’ nutrition environments.

          Methods

          Go NAPSACC retained core elements and implementation strategies from the original program, but translated tools into an online, self-directed format using extensive input from the ECE community. For the pilot, local technical assistance (TA) agencies facilitated recruitment of 33 centers, which were randomized to immediate (intervention, n = 18) or delayed (control, n = 15) access groups. Center directors were oriented on Go NAPSACC tools by their local TA providers (after being trained by researchers), after which they implemented Go NAPSACC independently with minimal TA support. The Environment and Policy Assessment and Observation instrument (self-report), collected prior to and following the 4-month intervention period, was used to assess impact on centers’ nutrition environments. Process data were also collected from a sample of directors and all TA providers to evaluate program usability and implementation.

          Results

          Demographic characteristics of intervention and control centers were similar. Two centers did not complete follow-up measures, leaving 17 intervention and 14 control centers in the analytic sample. Between baseline and follow-up, intervention centers improved overall nutrition scores (Cohen’s d effect size = 0.73, p = 0.15), as well as scores for foods (effect size = 0.74, p = 0.16), beverages (effect size = 0.54, p = 0.06), and menus (effect size = 0.73, p = 0.08), but changes were not statistically significant.

          Conclusions

          Core elements of NAPSACC were effectively translated into online tools and successfully implemented by center directors. Results suggest that the online program may have retained its ability to drive change in centers’ nutrition environments using a streamlined, self-directed, and flexible implementation approach. Results need to be confirmed in a larger more definitive trial.

          Trial registration

          NCT02889198 (retrospectively registered).

          Electronic supplementary material

          The online version of this article (10.1186/s12889-017-4898-z) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Defining Feasibility and Pilot Studies in Preparation for Randomised Controlled Trials: Development of a Conceptual Framework

          We describe a framework for defining pilot and feasibility studies focusing on studies conducted in preparation for a randomised controlled trial. To develop the framework, we undertook a Delphi survey; ran an open meeting at a trial methodology conference; conducted a review of definitions outside the health research context; consulted experts at an international consensus meeting; and reviewed 27 empirical pilot or feasibility studies. We initially adopted mutually exclusive definitions of pilot and feasibility studies. However, some Delphi survey respondents and the majority of open meeting attendees disagreed with the idea of mutually exclusive definitions. Their viewpoint was supported by definitions outside the health research context, the use of the terms ‘pilot’ and ‘feasibility’ in the literature, and participants at the international consensus meeting. In our framework, pilot studies are a subset of feasibility studies, rather than the two being mutually exclusive. A feasibility study asks whether something can be done, should we proceed with it, and if so, how. A pilot study asks the same questions but also has a specific design feature: in a pilot study a future study, or part of a future study, is conducted on a smaller scale. We suggest that to facilitate their identification, these studies should be clearly identified using the terms ‘feasibility’ or ‘pilot’ as appropriate. This should include feasibility studies that are largely qualitative; we found these difficult to identify in electronic searches because researchers rarely used the term ‘feasibility’ in the title or abstract of such studies. Investigators should also report appropriate objectives and methods related to feasibility; and give clear confirmation that their study is in preparation for a future randomised controlled trial designed to assess the effect of an intervention.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

            Background A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed. Objective This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions. Methods The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies. Results Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001). Conclusions The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988-2007.

              The use of computers to promote healthy behavior is increasing. To evaluate the efficacy of these computer-delivered interventions, we conducted a meta-analysis of the published literature. Studies examining health domains related to the leading health indicators outlined in Healthy People 2010 were selected. Data from 75 randomized controlled trials, published between 1988 and 2007, with 35,685 participants and 82 separate interventions were included. All studies were coded independently by two raters for study and participant characteristics, design and methodology, and intervention content. We calculated weighted mean effect sizes for theoretically-meaningful psychosocial and behavioral outcomes; moderator analyses determined the relation between study characteristics and the magnitude of effect sizes for heterogeneous outcomes. Compared with controls, participants who received a computer-delivered intervention improved several hypothesized antecedents of health behavior (knowledge, attitudes, intentions); intervention recipients also improved health behaviors (nutrition, tobacco use, substance use, safer sexual behavior, binge/purge behaviors) and general health maintenance. Several sample, study and intervention characteristics moderated the psychosocial and behavioral outcomes. Computer-delivered interventions can lead to improved behavioral health outcomes at first post-intervention assessment. Interventions evaluating outcomes at extended assessment periods are needed to evaluate the longer-term efficacy of computer-delivered interventions.
                Bookmark

                Author and article information

                Contributors
                dsward@email.unc.edu
                avaughn@email.unc.edu
                mazzucca@email.unc.edu
                reganb@email.unc.edu
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                21 November 2017
                21 November 2017
                2017
                : 17
                : 891
                Affiliations
                [1 ]ISNI 0000000122483208, GRID grid.10698.36, Department of Nutrition, Gillings School of Global Public Health, and Fellow, Center for Health Promotion and Disease Prevention, , University of North Carolina at Chapel Hill, ; 2202 McGavran-Greenberg Hall, CB 7461, Chapel Hill, NC 27599-7461 USA
                [2 ]ISNI 0000000122483208, GRID grid.10698.36, Center for Health Promotion and Disease Prevention, , University of North Carolina at Chapel Hill, ; 1700 Martin L. King Jr. Blvd., CB 7426, Chapel Hill, NC 27599-7426 USA
                [3 ]ISNI 0000000122483208, GRID grid.10698.36, Department of Nutrition, Gillings School of Global Public Health, , University of North Carolina at Chapel Hill, ; 1700 Martin L. King Jr. Blvd., CB 7426, Chapel Hill, NC 27599-7426 USA
                Article
                4898
                10.1186/s12889-017-4898-z
                5698966
                29162057
                62904593-8d18-4a36-87ed-2f1183b66d28
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 1 March 2017
                : 9 November 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000867, Robert Wood Johnson Foundation;
                Funded by: FundRef http://dx.doi.org/10.13039/100000056, National Institute of Nursing Research;
                Award ID: T32NR007091
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000030, Centers for Disease Control and Prevention;
                Award ID: U48-DP005017
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Public health
                web-based,children,nutrition environment,implementation
                Public health
                web-based, children, nutrition environment, implementation

                Comments

                Comment on this article