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Aim Identify the medication adherence determinants in older adults with multimorbidity and polypharmacy. Materials and Methods A cross‐sectional study was conducted in a non‐probabilistic sample of 245 adults ≥65 years recruited in a general medical ward of one teaching hospital. Data were collected during hospital stay using a face‐to‐face interview based on a set of validated questionnaires, such as the measure treatment adherence, the beliefs about medicines questionnaire‐specific and the geriatric depression scale. Descriptive and multiple linear regression analysis were performed. Results Participants' mean age was 78.32 (SD: 6.95) years and 50.6% were women. Older adults lived with an average of 7.51 (SD: 1.95) chronic conditions and had a mean of 7.95 (min. 4; max. 18) medications prescribed. The proportion of older adults adherent to medication was 43.7%. Depression ( β = −0.142; p = 0.031), beliefs about treatment necessity ( β = 0.306; p = 0.001) and concerns about the medication ( β = −0.204; p = 0.001) were found as independent determinants of adherence. Conclusion Self‐reported medication non‐adherence appears to be common in older adults with multimorbidity and polypharmacy. Depression, necessity and concerns should be considered when assessing medication non‐adherence in practice. This study will also contribute to develop an intervention to manage adherence in older people, as part of a doctoral research project.

Introduction: Improving adherence to antidiabetic medication is crucial, resulting in improved health outcomes, cost reduction, and minimization of waste. A lack of underlying theory in existing interventions may explain the limited success in sustaining behavior change. This paper describes the development of a theory and evidence-based complex intervention to improve adherence to oral antidiabetics in older people via a software prototype with an anthropomorphic virtual assistant. Methods: The Behavior Change Wheel (BCW) was used to develop a theoretical understanding of the change process, corresponding to the first phase of the Medical Research Council Framework for developing and evaluating complex interventions. At the BCW core is a model of human behavior (COM-B), which posits that human behavior (B) results from the interaction between capabilities (C), opportunities (O), and motivation (M). Literature-derived medication adherence determinants were mapped onto COM-B components. Then, intervention functions (IFs) were selected employing the APEASE criteria. Finally, standardized behavior change techniques (BCTs) were chosen based on their suitability and their effectiveness on medication adherence trials. The prototype was developed for android devices; its core was implemented in Unity3D, using a female 3D virtual assistant, named Vitória. Results: Two COM-B components were identified as main targets for behavior change—psychological capability and reflective motivation; these were linked with four IFs—education, persuasion, enablement, and environmental restructuring. Eleven BCTs were, in turn, linked with the IFs. An example of a BCT is “problem solving”; it requires users to pinpoint factors influencing non-adherence and subsequently offers strategies to achieve the desired behavior. BCTs were operationalized into the dialogues with Vitória and into supplementary software features. Vitória communicates with users verbally and non-verbally, expressing emotions. Input options consist of buttons or recording values, such as medication taken. Conclusion: The present approach enabled us to derive the most appropriate BCTs for our intervention. The use of an explicit bundle of BCTs, often overlooked in interventions promoting medication adherence, is expected to maximize effectiveness and facilitates replication. The first prototype is being refined with users and health professionals’ contributions. Future work includes subjecting the prototype to usability tests and a feasibility trial.

Resumo. Introdução: O desenvolvimento da investigação e a emergência de uma prática baseada na evidência são um desafio claro para a educação dos futuros profissionais de saúde, aos quais são requeridas competências de pesquisa, leitura e utilização correta dos resultados da investigação na prática clínica. Objetivos: Identificar o que aprendem os estudantes de enfermagem com o envolvimento em projetos de investigação, durante a licenciatura. Métodos: A revisão integrativa de literatura foi efetuada segundo um protocolo, com definição dos critérios de elegibilidade dos estudos primários, obtidos nas bases de dados dos motores de busca EBSCO, JBI e Scopus. Resultados: Os estudantes podem ser envolvidos em todas as fases do processo de pesquisa, contribuindo para a sua satisfação na aprendizagem, com impacto positivo na motivação, reflexão e integração do conhecimento, desenvolvimento de competências de comunicação, escrita científica, gestão do tempo e juízo critico. O envolvimento dos estudantes contribui para o desenvolvimento dos projetos de investigação. Conclusões: O envolvimento dos estudantes em projetos de investigação contribui para diferentes aprendizagens. Os nossos resultados sugerem que a formação teórica sobre investigação deve ser associada a outras estratégias para o desenvolvimento de conhecimentos, habilidades e atitudes de pesquisa.

Objective: To identify the learning outcomes and skills obtained of undergraduate nursing students involved in research projects. Methods: This was an integrative literature review, based on a research protocol in the CINAHL Complete databases; Cochrane Central Register of Controlled Trials; Cochrane Database of Systematic Reviews; Cochrane Methodology Register; MedicLatina; MEDLINE, Scopus and JBI, including primary and secondary studies, published between 2015 and 2020. Results: A total of five heterogeneous articles were included, which were categorized using Kirkpatrick's (adapted) model. Seventeen learning outcomes acquired through participation in research projects were identified, from the learning of new knowledge and skills to the development of new attitudes and behaviors. Final considerations: The involvement of nursing students in research projects is important to their professional development. Future investment in research on this topic can help cement the potential of this type of student involvement.

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Background Conversational agents, which we defined as computer programs that are designed to simulate two-way human conversation by using language and are potentially supplemented with nonlanguage modalities, offer promising avenues for health interventions for different populations across the life course. There is a lack of open-access and user-friendly resources for identifying research trends and gaps and pinpointing expertise across international centers. Objective Our aim is to provide an overview of all relevant evidence on conversational agents for health and well-being across the life course. Specifically, our objectives are to identify, categorize, and synthesize—through visual formats and a searchable database—primary studies and reviews in this research field. Methods An evidence map was selected as the type of literature review to be conducted, as it optimally corresponded to our aim. We systematically searched 8 databases (MEDLINE; CINAHL; Web of Science; Scopus; the Cochrane, ACM, IEEE, and Joanna Briggs Institute databases; and Google Scholar). We will perform backward citation searching on all included studies. The first stage of a double-stage screening procedure, which was based on abstracts and titles only, was conducted by using predetermined eligibility criteria for primary studies and reviews. An operational screening procedure was developed for streamlined and consistent screening across the team. Double data extraction will be performed with previously piloted data collection forms. We will appraise systematic reviews by using A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2. Primary studies and reviews will be assessed separately in the analysis. Data will be synthesized through descriptive statistics, bivariate statistics, and subgroup analysis (if appropriate) and through high-level maps such as scatter and bubble charts. The development of the searchable database will be informed by the research questions and data extraction forms. Results As of April 2021, the literature search in the eight databases was concluded, yielding a total of 16,351 records. The first stage of screening, which was based on abstracts and titles only, resulted in the selection of 1282 records of primary studies and 151 records of reviews. These will be subjected to second-stage screening. A glossary with operational definitions for supporting the study selection and data extraction stages was drafted. The anticipated completion date is October 2021. Conclusions Our wider definition of a conversational agent and the broad scope of our evidence map will explicate trends and gaps in this field of research. Additionally, our evidence map and searchable database of studies will help researchers to avoid fragmented research efforts and wasteful redundancies. Finally, as part of the Harnessing the Power of Conversational e-Coaches for Health and Well-being Through Swiss-Portuguese Collaboration project, our work will also inform the development of an international taxonomy on conversational agents for health and well-being, thereby contributing to terminology standardization and categorization. International Registered Report Identifier (IRRID) DERR1-10.2196/26680

This chapter describes the development of a theory-driven and evidence-based digital intervention to facilitate self-care in older adults with Type 2 Diabetes (T2D) and, additionally, its contribution to healthy aging and the individual care plan. T2D is highly prevalent in older adults. Difficulties in adopting and maintaining desirable self-care behaviors is associated with lack of glycemic control and subsequent complications, which significantly burden patients, their families, and the health system. The VASelfCare (Virtual Assistant Self-Care) intervention is a software application that provides an interface with a 3D anthropomorphic virtual assistant targeting three key self-care behaviors: medication-taking, physical activity, and a healthy diet. Other VASelfCare elements are intended for nurses providing diabetes consultations, including a web-based back-office with a patient data dashboard, which streamlines integration of care. The application prototype has been co-produced with older adults with T2D, primary care health professionals, and other stakeholders.