The Application of Systems Science in Nutrition-Related Behaviors and Outcomes Implementation Research: A Scoping Review

Advancing college food security: priority research gaps

Despite over a decade of both quantitative and qualitative studies, food insecurity among US college/university students remains a pervasive problem within higher education. The purpose of this perspective piece was to highlight research gaps in the area of college food insecurity and provide rationale for the research community to focus on these gaps going forward. A group of food insecurity researchers from a variety of higher education institutions across the United States identified five thematic areas of research gaps: screening and estimates of food insecurity; longitudinal changes in food insecurity; impact of food insecurity on broader health and academic outcomes; evaluation of impact, sustainability and cost effectiveness of existing programmes and initiatives; and state and federal policies and programmes. Within these thematic areas, nineteen specific research gaps were identified that have limited or no peer-reviewed, published research. These research gaps result in a limited understanding of the magnitude, severity and persistence of college food insecurity, the negative short- and long-term impacts of food insecurity on health, academic performance and overall college experience, and effective solutions and policies to prevent or meaningfully address food insecurity among college students. Research in these identified priority areas may help accelerate action and interdisciplinary collaboration to alleviate food insecurity among college students and play a critical role in informing the development or refinement of programmes and services that better support college student food security needs.

Application of the Theory of Planned Behaviour to inform development of a Dissemination and Implementation science training for nutrition practitioners

OBJECTIVE

To determine nutrition practitioners' attitudes, behavioural control and normative beliefs to best inform the development and formulation of a nutrition-specific Dissemination and Implementation (D&I) science training.

DESIGN

A cross-sectional survey aimed to assess Theory of Planned Behaviour (TPB) constructs and intention to use D&I science. A validated TPB questionnaire assessed constructs including perceived behavioural control, subjective, injunctive and descriptive normative beliefs, attitudes and intention to use D&I science. For analysis, Spearman's ρ, Kruskal-Wallis and Steel-Dwass tests were conducted for quantitative variables.

SETTING

Online, 26-item Qualtrics survey.

PARTICIPANTS

Cross-sectional sample of members (n 70) affiliated with the Society for Nutrition Education and Behaviour listserv.

RESULTS

The major finding from this study was a significant positive correlation between perceived behavioural control score and intention (r = 0·315, P = 0·0119).

CONCLUSIONS

D&I training interventions could formulate learning and teaching strategies to target perceived behavioural control (self-efficacy, knowledge and ability) to enhance intention. For example, application and experience-based learning techniques trainings could be strategies to increase knowledge and abilities.

System dynamics modeling for cancer prevention and control: A systematic review

Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed.

Implementation of Diabetes Education and Support in Appalachia

Limited research on diabetes education and support implementation in Appalachia, which is a critical knowledge gap considering barriers to care, and high prevalence rates. The aim was to understand what each facility is providing regarding diabetes education and services within West Virginia. This study reports cognitive interview qualitative findings from a multi-methods study. Individuals were recruited through an online search to identify clinics, organizations, and hospital staff that provided diabetes education in the state of West Virginia. Eligible participants were individuals who facilitated and managed diabetes education and support in counties of West Virginia. The interviews followed an 11-item interview guide, approved, and reviewed by a practicing Registered Dietitian and Certified Diabetes Care Education Specialist in West Virginia. All qualitative data from the interviews were hand-coded using grounded theory, by two researchers. 15 participating organizations from the state of West Virginia were included and described three phenomena: Diabetes Education Implementation (differences in: evaluation measures, modality, delivery format, topical areas); Barriers to Care (staffing, lack of training, evaluation, loss of research partnerships and funding); and Facilitators to Care (community-based involvement, interdisciplinary collaboration, capacity building (trainings). There are concerns with program drift and "risky" adaptations such as inconsistent evaluative measures, lack of training for program facilitators, variety of delivery formats, and content material. Findings recommend more alignment in program delivery to better implementation. Further studies should assess patient experiences with implemented diabetes education programs in West Virginia to further support the current research findings.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.