Objective To reduce health disparities, behavioral health interventions must reach subcultural groups and demonstrate effectiveness in improving their health behaviors and outcomes. One approach to developing such health interventions is to culturally adapt original evidence-based interventions. The goals of the paper are to (a) describe consensus on the stages involved in developing cultural adaptations, (b) identify common elements in cultural adaptations, (c) examine evidence on the effectiveness of culturally enhanced interventions for various health conditions, and (d) pose questions for future research. Method Influential literature from the past decade was examined to identify points of consensus. Results There is agreement that cultural adaptation can be organized into five stages: information gathering, preliminary design, preliminary testing, refinement, and final trial. With few exceptions, reviews of several health conditions (e.g., AIDS, asthma, diabetes) concluded that culturally enhanced interventions are more effective in improving health outcomes than usual care or other control conditions. Conclusion Progress has been made in establishing methods for conducting cultural adaptations and providing evidence of their effectiveness. Future research should include evaluations of cultural adaptations developed in stages, tests to determine the effectiveness of cultural adaptations relative to the original versions, and studies that advance our understanding of cultural constructs’ contributions to intervention engagement and efficacy.

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This study examines the influence of sources and types of social support on youth physical activity. The sample comprised 372 youth (mean age=12.05 years, SD=1.63). Youth were 76% White and 50.3% female. The annual household income for the sample was 20% under $30,000; 30% $30,000-$49,999; 25% $50,000-$69,999; 13% $70,000-$89,999; and 12% $90,000 and above. Results revealed that friends who support and watch youth engage in activities are significantly and positively related to youth physical activity. Significant correlations existed among the type factors. Future research should examine the sources and nature of support and the mechanisms through which social support influences youth physical activity.

Objective Internet-based programs offer potential for practical, cost-effective chronic illness self-management programs. Methods We report 12-month results of an Internet-based diabetes self-management program, with and without additional support, compared to enhanced usual care in a 3-arm practical randomized trial. Patients (n= 463) were randomized: 77.3% completed 12-month follow-up. Primary outcomes were changes in health behaviors of healthy eating, physical activity, and medication taking. Secondary outcomes were hemoglobin A1c, body mass index, lipids, blood pressure, and psychosocial factors. Results Internet conditions improved health behaviors significantly vs. usual care over the 12-month period (d for effect size = .09 – .16). All conditions improved moderately on biological and psychosocial outcomes. Latinos, lower literacy, and higher cardiovascular disease risk patients improved as much as other participants. Conclusions The Internet intervention meets the reach and feasibility criteria for a potentially broad public health impact. However, 12-month magnitude of effects was small, suggesting that different or more intensive approaches are necessary to support long-term outcomes. Research is needed to understand the linkages between intervention and maintenance processes and downstream outcomes. Practice Implications Automated self-management interventions should be tailored and integrated into primary care; maintenance of patient self-management can be enhanced through links to community resources.

Aims To identify the unique sources of diabetes distress (DD) for adults with type 1 diabetes (T1D). Methods Sources of DD were developed from qualitative interviews with 25 T1D adults and 10 diabetes health care providers. Survey items were then developed and analyzed using both exploratory (EFA) and confirmatory CFA) analyses on two patient samples. Construct validity was assessed by correlations with depressive symptoms (PHQ8), complications, HbA1C, BMI, and hypoglycemia worry scale (HWS). Scale cut-points were created using multiple regression. Results An EFA with 305 U.S. participants yielded 7 coherent, reliable sources of distress that were replicated by a CFA with 109 Canadian participants: Powerlessness, Negative Social Perceptions, Physician Distress, Friend/Family Distress, Hypoglycemia Distress, Management Distress, Eating Distress. Prevalence of DD was high with 41.6% reporting at least moderate DD. Higher DD was reported for women, those with complications, poor glycemic control, younger age, without a partner, and non-White patients. Conclusions We identified a profile of seven major sources of DD among T1D using a newly developed assessment instrument. The prevalence of DD is high and is related to glycemic control and several patient demographic and disease-related patient characteristics, arguing for a need to address DD in clinical care.

OBJECTIVETo clarify previous findings that diabetes distress is related to glycemic control and self-management whereas measures of depression are not, using both binary and continuous measures of depression.RESEARCH DESIGN AND METHODSFour hundred and sixty-three type 2 patients completed measures of diabetes distress (Diabetes Distress Scale [DDS]) and clinical depression (Patient Health Questionnaire 8 [PHQ8]). PHQ8 was employed as either a binary (≥10) or continuous variable. Dependent variables were A1C, diet, physical activity (PA), and medication adherence (MA).RESULTSThe inclusion of a binary or continuous PHQ8 score yielded no differences in any equation. DDS was significantly associated with A1C and PA, whereas PHQ8 was not; both DDS and PHQ8 were significantly and independently associated with diet and MA.CONCLUSIONSThe lack of association between depression and glycemic control is not due to the use of a binary measure of depression. Findings further clarify the significant association between distress and A1C.

OBJECTIVETo evaluate associations between psychosocial and social-environmental variables and diabetes self-management, and diabetes control.RESEARCH DESIGN AND METHODSBaseline data from a type 2 diabetes self-management randomized trial with 463 adults having elevated BMI (M = 34.8 kg/m2) were used to investigate relations among demographic, psychosocial, and social-environmental variables; dietary, exercise, and medication-taking behaviors; and biologic outcomes.RESULTSSelf-efficacy, problem solving, and social-environmental support were independently associated with diet and exercise, increasing the variance accounted for by 23 and 19%, respectively. Only diet contributed to explained variance in BMI (β = −0.17, P = 0.0003) and self-rated health status (β = 0.25, P < 0.0001); and only medication-taking behaviors contributed to lipid ratio (total–to–HDL) (β = −0.20, P = 0.0001) and A1C (β = −0.21, P < 0.0001).CONCLUSIONSInterventions should focus on enhancing self-efficacy, problem solving, and social-environmental support to improve self-management of diabetes.

Background Increased access to the Internet and the availability of efficacious eHealth interventions offer great promise for assisting adults with diabetes to change and maintain health behaviors. A key concern is whether levels of engagement in Internet programs are sufficient to promote and sustain behavior change.Objective This paper used automated data from an ongoing Internet-based diabetes self-management intervention study to calculate various indices of website engagement. The multimedia website involved goal setting, action planning, and self-monitoring as well as offering features such as “Ask an Expert” to enhance healthy eating, physical activity, and medication adherence. We also investigated participant characteristics associated with website engagement and the relationship between website use and 4-month behavioral and health outcomes.Methods We report on participants in a randomized controlled trial (RCT) who were randomized to receive (1) the website alone (n = 137) or (2) the website plus human support (n = 133) that included additional phone calls and group meetings. The website was available in English and Spanish and included features to enhance engagement and user experience. A number of engagement variables were calculated for each participant including number of log-ins, number of website components visited at least twice, number of days entering self-monitoring data, number of visits to the “Action Plan” section, and time on the website. Key outcomes included exercise, healthy eating, and medication adherence as well as body mass index (BMI) and biological variables related to cardiovascular disease risk.Results Of the 270 intervention participants, the average age was 60, the average BMI was 34.9 kg/m2, 130 (48%) were female, and 62 (23%) self-reported Latino ethnicity. The number of participant visits to the website over 4 months ranged from 1 to 119 (mean 28 visits, median 18). Usage decreased from 70% of participants visiting at least weekly during the first 6 weeks to 47% during weeks 7 to 16. There were no significant differences between website only and website plus support conditions on most of the engagement variables. In total, 75% of participants entered self-monitoring data at least once per week. Exercise action plan pages were visited more often than medication taking and healthy eating pages (mean of 4.3 visits vs 2.8 and 2.0 respectively, P < .001). Spearman nonparametric correlations indicated few significant associations between patient characteristics and summary website engagement variables, and key factors such as ethnicity, baseline computer use, age, health literacy, and education were not related to use. Partial correlations indicated that engagement, especially in self-monitoring, was most consistently related to improvement in healthy eating (r = .20, P = .04) and reduction of dietary fat (r = -.31, P = .001). There was also a significant correlation between self-monitoring and improvement in exercise (r = .20, P = .033) but not with medication taking.Conclusion...