http://academic.oup.com/tbm en-us Wed, 18 Dec 2024 00:00:00 GMT Wed, 18 Dec 2024 08:47:09 GMT Silverchair https://academic.oup.com/tbm/advance-article/doi/10.1093/tbm/ibae072/7927644?rss=1 Wed, 18 Dec 2024 00:00:00 GMT <span class="paragraphSection"><div class="boxTitle">Abstract</div>Emerging evidence suggests that bi-directional communication and referral pathways, when employed strategically, can lead to favorable health outcomes by connecting patients with complex, multi-faceted health and social needs to appropriate services and resources. However, despite these benefits, patient acceptance of referrals via these pathways remains suboptimal. In this study, we describe individual and clinical factors associated with patient acceptance of these referrals. We extracted individual-level demographic and clinical data for patients referred primarily from a large safety-net health system to a multi-purpose resource hub co-located on the campus of its largest hospital, for the period October 2019 to June 2023. Descriptive statistics, Chi-square analyses, and multinomial regression modeling were performed to examine these data. Of 1865 patients in the study sample, 54.2% accepted a referral, 27.4% were lost to follow-up, and 18.4% declined. Most patients who accepted referrals were female (67.1%), Latino (81.5%), and had hypertension and/or prediabetes or diabetes (84.1%). In modeling analyses, those who accepted referrals tended to be female, and were referred from primary care clinics; many were referred for multiple service/resource categories. We found associations between patient acceptance of referrals and gender and source of referral. Drawing upon these results as well as experience implementing these systems, we propose several practical strategies for increasing successful referrals, including identifying and addressing barriers for patients who declined or were lost to follow-up; using standardized screening tools to routinely assess for multi-faceted health and social needs; increasing provider awareness about the benefits and functioning of these pathways; and monitoring progress so mid-course adjustments can be made when necessary.</span> ibae072 10.1093/tbm/ibae072 http://doi.org/10.1093/tbm/ibae072 https://academic.oup.com/tbm/advance-article/doi/10.1093/tbm/ibae068/7926630?rss=1 Tue, 17 Dec 2024 00:00:00 GMT <span class="paragraphSection"><div class="boxTitle">Abstract</div>While digital cardiac rehabilitation (CR) is an effective alternative to center-based CR, its components and mechanisms of change remain poorly understood. The Multiphase Optimization Strategy (MOST) provides a framework that allows the effects of individual components of complex interventions to be studied. There is limited guidance within MOST on how to develop a conceptual model. This article describes the development of a conceptual model of digital CR. The conceptual model was developed based on several strands of evidence: (i) a systematic review of 25 randomized controlled trials to identify the behavior change techniques in digital CR interventions, (ii) a qualitative study of patients’ (<span style="font-style:italic;">n</span> = 11) perceptions of the mechanisms of digital CR, and (iii) a review of international guidelines. Tools and frameworks from behavioral science, including the Behaviour Change Wheel, Capability, Opportunity, Motivation and Behavior model, and Theoretical Domains Framework were used to integrate the findings. An initial conceptual model of digital CR was developed and then refined through discussion. The conceptual model outlines the causal process through which digital CR can enhance outcomes for patients with cardiovascular disease. The model illustrates the key intervention components (e.g. goal setting and self-monitoring, education, exercise training), targeted outcomes (e.g. physical activity, healthy eating, medication adherence), and theorized mediating variables (e.g. knowledge, beliefs about capability). The article provides an example of how behavioral science frameworks and tools can inform the preparation phase of MOST. The developed conceptual model of digital CR will inform guide decision-making in a future optimization trial.</span> ibae068 10.1093/tbm/ibae068 http://doi.org/10.1093/tbm/ibae068