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      Effect of a community-based diabetes self-management empowerment program on mental health-related quality of life: a causal mediation analysis from a randomized controlled trial

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          Abstract

          Background

          There is a paucity of evidence supporting the effectiveness of diabetes self-management education (DSME) in improving mental health-related quality of life (HRQoL) for African American and Latinos. Also, among studies supporting the favorable effects of DSME on mental HRQoL, the direct effect of DSME that is independent of improved glycemic control has never been investigated. The objectives of this study were to investigate the effect of community-based DSME intervention targeting empowerment on mental HRQoL and to determine whether the effect is direct or mediated by glycemic control.

          Methods

          We conducted secondary analyses of data from the Diabetes Self-Care Study, a randomized controlled trial of a community-based DSME intervention. Study participants (n = 516) were African Americans and Latinos 55 years or older with poorly controlled diabetes (HbA1c ≥ 8.0%) recruited from senior centers and churches in Los Angeles. The intervention group received six weekly small-group self-care sessions based on the empowerment model. The control group received six lectures on unrelated geriatrics topics. The primary outcome variable in this secondary analysis was the change in Mental Component Summary score (MCS-12) from the SF-12 Health Survey between baseline and six-month follow-up. We used the change in HbA1c during the study period as the main mediator of interest in our causal mediation analysis. Additionally, possible mediations via social support and perceived empowerment attributable to the program were examined.

          Results

          MCS-12 increased by 1.4 points on average in the intervention group and decreased by 0.2 points in the control group (difference-in-change: 1.6 points, 95% CI: 0.1 to 3.2). In the causal mediation analysis, the intervention had a direct effect on MCS-12 improvement (1.7 points, 95% CI: 0.2 to 3.2) with no indirect effects mediated via HbA1c change (−0.1 points, 95% CI: −0.4 to 0.1), social support (0.1 points), and perception of empowerment (0.1 points).

          Conclusions

          This Diabetes Self-Care Study empowerment intervention had a modest positive impact on mental HRQoL not mediated by the improvement in glycemic control, as well as social support and perception of empowerment. This favorable effect on mental HRQoL may be a separate clinical advantage of this DSME intervention.

          Trial Registration

          ClinicalTrial.gov NCT00263835.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12913-015-0779-2) contains supplementary material, which is available to authorized users.

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          Most cited references 32

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          The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

          In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
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            A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity.

            Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
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              Mediation analysis.

              Mediating variables are prominent in psychological theory and research. A mediating variable transmits the effect of an independent variable on a dependent variable. Differences between mediating variables and confounders, moderators, and covariates are outlined. Statistical methods to assess mediation and modern comprehensive approaches are described. Future directions for mediation analysis are discussed.
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                Author and article information

                Contributors
                tsugiyama-tky@umin.ac.jp
                NSteers@mednet.ucla.edu
                NWenger@mednet.ucla.edu
                kduru@mednet.ucla.edu
                CMangione@mednet.ucla.edu
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                22 March 2015
                22 March 2015
                2015
                : 15
                Affiliations
                [ ]Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, 3rd Floor, 911 Broxton Ave, Los Angeles, California 90024 USA
                [ ]Department of Clinical Study and Informatics, Center for Clinical Sciences, National Center for Global Health and Medicine, 1-21-1 Toyama Shinjuku-ku, Tokyo, 162-8655 Japan
                [ ]Department of Public Health/Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Tokyo, Bunkyo-ku 113-0033 Japan
                [ ]Department of Health Policy and Management, UCLA Fielding School of Public Health, 640 Charles E Young Dr S, Los Angeles, California 90024 USA
                Article
                779
                10.1186/s12913-015-0779-2
                4375843
                © Sugiyama et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

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