Agent-Based Modeling: A Method for Investigating Challenging Research Problems.

BACKGROUND: For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question. OBJECTIVES: The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science. METHODS: An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented. RESULTS: The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals. DISCUSSION: ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.

Health Literacy and Serious or Persistent Mental Illness: A Mixed Methods Study.

BACKGROUND: Health literacy is increasingly recognized as a major determinant of health; however, our insights into the health literacy strengths and needs of adults living with serious or persistent mental illness remain limited by a notable lack of research in this area. Improving our understanding is important because people in this group are especially vulnerable to numerous negative health outcomes, many preventable. OBJECTIVE: To assess the health literacy strengths and needs of people living with serious or persistent mental illness in terms of their ability to acquire, understand, and use information about their illness and the health services they require. METHODS: A cross-sectional convergent mixed methods design guided by the Ophelia Access and Equity Framework. People diagnosed with serious or persistent mental illness were offered participation. Quantitative and qualitative data was collected using questionnaires (Health Literacy Questionnaire [HLQ], World Health Organization [WHO-5]) and semi-structured interviews. Hierarchical cluster analysis identified and grouped participants with similar health literacy scores into mutually exclusive groups, for the development of clinical vignettes. KEY RESULTS: Participants struggled most with the appraisal of health information (HLQ mean 2.72, standard deviation [SD] .63 [scale 1-4]) and navigating what they often perceived to be a confusing health care system (HLQ mean 3.29, SD .79 [scale 1-5]). On the other hand, most participants reported positive experiences with their health care providers (HLQ mean 3.19, SD .62 [scale 1-4]) and generally felt understood and supported. The cluster analysis suggests we should not assume people living with serious or persistent mental illness have homogeneous HL strengths and needs, meaning a one-size-fits-all solution for improving health literacy in this diverse group will likely not be a successful strategy. It will be important to explore solutions that embrace patient-centered care approaches. CONCLUSIONS: This study is one of only a handful assessing the health literacy strengths and needs of people living with serious or persistent mental illness. By collecting both quantitative and qualitative data, then analyzing the results using sophisticated cluster analysis methods, the authors were able to develop clinical vignettes per the Ophelia Framework that offer results in a practical way that can be readily understood and acted upon by stakeholders. We found that the HLQ is a measure of HL that is acceptable to mental health clients, and our findings provide preliminary data on the use of this instrument in the mental health population. [HLRP: Health Literacy Research and Practice. 2023;7(1):e2-e13.] Plain Language Summary: This study explored the health literacy strengths and needs of people living with serious or persistent mental illness. The results showed a mix of strengths and needs among our participants, though several consistent themes emerged. Most of our participants felt understood and supported by their health care providers, but many often struggle with judging the quality of health information and finding their way through the health care system.

mHealth Apps as Effective Persuasive Health Technology: Contextualizing the "Necessary" Functionalities.

Persuasive health technology (PHT) is any technology purposely designed to influence, reinforce, change, or shape health-related attitudes or behaviors. Behavioral interventions can be developed for the purpose of maintaining or improving a person's health status. Delivering behavioral interventions via PHTs is a promising approach for encouraging healthy behaviors among individuals and populations. Important attributes of all PHTs include their functionalities. A functionality refers to any useful features, functions, capabilities, or technologies associated with computer hardware or software. Creating effective PHTs requires a deliberate selection of appropriate functionalities for supporting specific behavioral interventions. The number and types of functionalities necessary to create an effective PHT will be specific to the context of each project, influenced by project objectives, stakeholder goals, behavioral interventions, and a variety of real-world constraints. Selecting appropriate functionalities can be challenging. Fortunately, there are frameworks and models developed specifically for guiding the design of PHTs. The Persuasive Systems Design model describes 4 categories, and 28 design principles for creating effective persuasive interventions. These same design principles could also be useful for guiding the selection of appropriate functionalities.

Simulation Modeling as a Novel and Promising Strategy for Improving Success Rates With Research Funding Applications: A Constructive Thought Experiment.

Writing a successful grant or other funding applications is a requirement for continued employment, promotion, and tenure among nursing faculty and researchers. Writing successful applications is a challenging task, with often uncertain results. The inability to secure funding not only threatens the ability of nurse researchers to conduct relevant health care research but may also negatively impact their career trajectories. Many individuals and organizations have offered advice for improving success with funding applications. While helpful, those recommendations are common knowledge and simply form the basis of any well-considered, well-formulated, and well-written application. For nurse researchers interested in taking advantage of innovative computational methods and leading-edge analytical techniques, we propose adding the results from computer-based simulation modeling experiments to funding applications. By first conducting a research study in a virtual space, nurse researchers can refine their study design, test various assumptions, conduct experiments, and better determine which elements, variables, and parameters are necessary to answer their research question. In short, simulation modeling is a learning tool, and the modeling process helps nurse researchers gain additional insights that can be applied in their real-world research and used to strengthen funding applications. Simulation modeling is well-suited for answering quantitative research questions. Still, the design of these models can benefit significantly from the addition of qualitative data and can be helpful when simulating the results of mixed methods studies. We believe this is a promising strategy for improving success rates with funding applications, especially among nurse researchers interested in contributing new knowledge supporting the paradigm shift in nursing resulting from advances in computational science and information technology.

'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling.

INTRODUCTION: Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP. METHODS: A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact. RESULTS: Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term. DISCUSSION: Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated.

Protocol for a systematic review on interventions for caregivers of persons with mild cognitive impairment and early dementia: does early stage intervention improve caregiver well-being and ability to provide care?

INTRODUCTION: Caregivers of persons with dementia and mild cognitive impairment (MCI) are at risk of decreased well-being. While many interventions for caregivers exist, evidence is sparse regarding intervention timing and effectiveness at an early stage of cognitive decline. Our systematic review aims to answer the following questions: (1) Do interventions for caregivers of persons with early stage dementia or MCI affect their well-being and ability to provide care? (2) Are particular types of caregiver interventions most effective during early stage cognitive decline? (3) How does effectiveness differ when early and later interventions are directly compared? (4) Do effects of early stage caregiver intervention vary based on care recipient and caregiver characteristics (eg, sex, type of dementia)? METHODS AND ANALYSIS: The databases MEDLINE, EMBASE, PSYCINFO and CINAHL, as well as grey literature databases, will be searched for English language studies using search terms related to caregiver interventions and dementia/MCI. Abstracts and full texts will be screened by two independent reviewers; included studies must assess the effects of an intervention for caregivers of persons with early stage dementia or MCI on caregiver well-being or ability to provide care. Intervention, study and participant characteristics will be extracted by two independent reviewers, along with outcome data. Risk of bias will be assessed using the Cochrane risk of bias tool (for controlled trials with and without randomisation). Interventions will be grouped by type (eg, psychoeducational) and a narrative synthesis is planned due to expected heterogeneity, but a meta-analysis will be performed where possible. The Grading of Recommendations, Assessment, Development and Evaluations approach will be used to inform conclusions regarding the quality of evidence for each type of intervention. ETHICS AND DISSEMINATION: Findings from this review will be disseminated via conferences and peer-reviewed publication, and a summary will be provided to the Alzheimer Society. PROSPERO REGISTRATION NUMBER: CRD42018114960.

Building Research Capacity: Results of a Feasibility Study Using a Novel mHealth Epidemiological Data Collection System Within a Gestational Diabetes Population.

Public health researchers have traditionally relied on individual self-reporting when collecting much epidemiological surveillance data. Data acquisition can be costly, difficult to acquire, and the data often notoriously unreliable. An interesting option for the collection of individual health (or indicators of individual health) data is the personal smartphone. Smartphones are ubiquitous, and the required infrastructure is well-developed across Canada, including many remote areas. Researchers and health professionals are asking themselves how they might exploit increasing smartphone uptake for the purposes of data collection, hopefully leading to improved individual and public health. A novel smartphone-based epidemiological data collection and analysis system has been developed by faculty and students from the CEPHIL (Computational Epidemiology and Public Health Informatics) Lab in the Department of Computer Science at the University of Saskatchewan. A pilot feasibility study was then designed to examine possible relationships between smartphone sensor data, surveys and individual clinical data within a population of pregnant women. The study focused on the development of Gestational Diabetes (GDM), a transient condition during pregnancy, but with serious potential post-birth complications for both mother and child. The researchers questioned whether real-time smartphone data could improve the clinical management and outcomes of women at risk for developing GDM, enabling earlier treatment. The initial results from this small study did not show improved prediction of GDM, but did demonstrate that real-time individual health and sensor data may be readily collected and analyzed efficiently while maintaining confidentiality. Because the original version of the data collection software could only run on Android phones, this often meant the study participants were required to carry two phones, and this often meant the study phone was not carried, and therefore data not collected. The lessons learned will greatly inform future research.

Effect of exercise training on VO2peak and left ventricular systolic function in recent cardiac transplant recipients.

This study examined the effect of 12 weeks of combined aerobic and resistance training on aerobic capacity, left ventricular ejection fraction, and arterial afterload during submaximal cycle exercise in 18 recent (<1 month from transplantation) cardiac transplant recipients. The main finding of this study is that 12 weeks of combined aerobic and resistance training is an effective intervention to increase aerobic capacity in this population. However, exercise training was not associated with favorable improvements in left ventricular systolic function, because stroke volume and ejection fraction were reduced after training because of an increase in arterial afterload.