Adaptations of evidence-based interventions (EBIs) often occur. However, little is known about the reasons for adaptation, the adaptation process, and outcomes of adapted EBIs. To address this gap, we conducted a systematic review to answer the following questions: (1) What are the reasons for and common types of adaptations being made to EBIs in community settings as reported in the published literature? (2) What steps are described in making adaptations to EBIs? and (3) What outcomes are assessed in evaluations of adapted EBIs? We conducted a systematic review of English language publications that described adaptations of public health EBIs. We searched Ovid PubMed, PsycINFO, PsycNET, and CINAHL and citations of included studies for adapted public health EBIs. We abstracted characteristics of the original and adapted populations and settings, reasons for adaptation, types of modifications, use of an adaptation framework, adaptation steps, and evaluation outcomes. Forty-two distinct EBIs were found focusing on HIV/AIDS, mental health, substance abuse, and chronic illnesses. More than half (62%) reported on adaptations in the USA. Frequent reasons for adaptation included the need for cultural appropriateness (64.3%), focusing on a new target population (59.5%), and implementing in a new setting (57.1%). Common adaptations were content (100%), context (95.2%), cultural modifications (73.8%), and delivery (61.9%). Most study authors conducted a community assessment, prepared new materials, implemented the adapted intervention, evaluated or planned to evaluate the intervention, determined needed changes, trained staff members, and consulted experts/stakeholders. Most studies that reported an evaluation (k = 36) included behavioral outcomes (71.4%), acceptability (66.7%), fidelity (52.4%), and feasibility (52.4%). Fewer measured adoption (47.6%) and changes in practice (21.4%). These findings advance our understanding of the patterns and effects of modifications of EBIs that are reported in published studies and suggest areas of further research to understand and guide the adaptation process. Furthermore, findings can inform better reporting of adapted EBIs and inform capacity building efforts to assist health professionals in adapting EBIs.

Evidence-based public health translation of research to practice is essential to improve the public’s health. Dissemination and implementation researchers have explored what happens once practitioners adopt evidence-based interventions (EBIs) and have developed models and frameworks to describe the adaptation process. This scoping study identified and summarized adaptation frameworks in published reports and grey literature. We followed the recommended steps of a scoping study: (a) identifying the research question; (b) identifying relevant studies; (c) selecting studies; (d) charting the data; (e) collating, summarizing, and reporting the results; and (f) consulting with experts. We searched PubMed, PsycINFO, PsycNET, and CINAHL databases for articles referencing adaptation frameworks for public health interventions in the published and gray literature, and from reference lists of framework articles. Two reviewers independently coded the frameworks and their steps and identified common steps. We found 13 adaptation frameworks with 11 program adaptation steps: (a) assess community, (b) understand the EBI(s), (c) select the EBI, (d) consult with experts, (e) consult with stakeholders, (f) decide on needed adaptations, (g) adapt the original EBI, (h) train staff, (i) test the adapted materials, (j) implement the adapted EBI, and (k) evaluate. Eight of these steps were recommended by more than five frameworks: #1–3, 6–7, and 9–11. This study is the first to systematically identify, review, describe, and summarize frameworks for adapting EBIs. It contributes to the literature by consolidating key steps in program adaptation of EBIs and describing the associated tasks in each step.

Use of the Internet to retrieve health information is increasingly common. The authors surveyed 743 undergraduate students at 2 academic institutions to examine their Internet use, health-seeking behaviors, and attitudes related to the use of the Internet to obtain health information. Fifty-three percent of the respondents indicated that they would like to get health information online, and 28% reported that they would like to attend a health program online. Overall, 74% of the students reported having ever received health information online, and more than 40% reported that they frequently searched the Internet for information. They used various search engines and multiple Web sites to find health information. Issues related to the credibility of the information on health Web sites were crucial considerations for students. The study found differences in Internet use for health information by gender and by level of Internet experience.

Numerous agencies are providing training, technical assistance, and other support to build community-based practitioners’ capacity to adopt and implement evidence-based prevention interventions. Yet, little is known about how best to design capacity-building interventions to optimize their effectiveness. Wandersman et al. (Am J Community Psychol.50:445–59, 2102) proposed the Evidence-Based System of Innovation Support (EBSIS) as a framework to guide research and thereby strengthen the evidence base for building practitioners’ capacity. The purpose of this review was to contribute to further development of the EBSIS by systematically reviewing empirical studies of capacity-building interventions to identify (1) the range of strategies used, (2) variations in the way they were structured, and (3) evidence for their effectiveness at increasing practitioners’ capacity to use evidence-based prevention interventions. PubMed, EMBASE, and CINAHL were searched for English-language articles reporting findings of empirical studies of capacity-building interventions that were published between January 2000 and January 2014 and were intended to increase use of evidence-based prevention interventions in non-clinical settings. To maximize review data, studies were not excluded a priori based on design or methodological quality. Using the EBSIS as a guide, two researchers independently extracted data from included studies. Vote counting and meta-summary methods were used to summarize findings. The review included 42 publications reporting findings from 29 studies. In addition to confirming the strategies and structures described in the EBSIS, the review identified two new strategies and two variations in structure. Capacity-building interventions were found to be effective at increasing practitioners’ adoption (n = 10 of 12 studies) and implementation (n = 9 of 10 studies) of evidence-based interventions. Findings were mixed for interventions’ effects on practitioners’ capacity or intervention planning behaviors. Both the type and structure of capacity-building strategies may have influenced effectiveness. The review also identified contextual factors that may require variations in the ways capacity-building interventions are designed. Based on review findings, refinements are suggested to the EBSIS. The refined framework moves the field towards a more comprehensive and standardized approach to conceptualizing the types and structures of capacity-building strategies. This standardization will assist with synthesizing findings across studies and guide capacity-building practice and research.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: With increased access to technology and the Internet there are many opportunities for utilizing e-health, Internet or technology-delivered health services and information, for the prevention and management of chronic diseases. Objective: The purpose of this paper is to explore (1) the differences in technology use, (2) online health information seeking and use behaviors, (3) attitudes towards seeking health information online, and (4) level of e-health literacy between adults aged 18-64 with and without chronic disease(s). Methods: A cross-sectional, Internet survey was conducted in March 2017 with 401 U.S adults. Participant responses were examined to understand associations between chronic disease status and e-health behaviors such as Internet health-seeking behaviors and online behaviors related to health, tracking health indicators with a mobile app, patient portal use, and preferences for health information. Results: About one in three (37.2%) participants reported at least one chronic disease diagnosis. Around 75% of all participants reported having ever searched for health information online. Participants with a chronic disease reported significantly higher instances of visiting and talking to a healthcare provider based on health information found online (40.0% vs. 25.8%, X2(2)=6.65, p=.01; 43.3% vs. 27.9%, X2(2)=7.58, p=.006). The uses of health information found online also significantly differed between participants with and without chronic diseases in affecting a decision about how to treat an illness or condition (49.2% vs. 35.0%, X2(3)=6.66, p=.04), changing the way they cope with a chronic condition or manage pain (40.8% vs. 19.4%, X2(2)=16.33, P<.001), and leading them to ask a doctor new questions or get a second opinion (37.5% vs. 19.6%, X2(2)=11.8, p<.001). Chronic disease participants were significantly more likely to be tracking health indicators (43.9% vs. 28.3%, X2(2) = 10.42, p=.006). Around 24% of all participants reported using a health related mobile app. In addition, participants with a chronic disease diagnosis reported significantly higher rates of patient portal access (55.0% vs 42.1%, X2(2)=6.33, p=.01) and use (40.9% vs 21%, X2(2)=18.24, P<.001). Lastly, both groups reported similar perceived skills in using the Internet for health information on the eHEALS health literacy scale. Higher eHEALS average scores also were significantly associated with e-health behavior engagement in both groups. The majority of participants responded positively when asked about the usefulness of health information and importance of accessing health resources online. Conclusions: The high rates of reported information seeking and use of Internet-based health technology among participants with chronic disease may reflect the uptake in e-health to help manage chronic disease conditions. Healthcare providers and educators should continue to seek ways to interact and support patients in their management of chronic disease through e-health platforms, including capitalizing on online resources, patient portals, and smartphone apps for disease education and monitoring.