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- All Subjects: Behavioral Sciences
- All Subjects: school meals
- Creators: Martinelli, Sarah
We surveyed a diverse group of Arizona residents, including over 2,300 parents of school-age children and nearly 1,300 members of the school community, consisting of teachers, lunchroom staff, school administrators, and other school employees. Respondents represented a wide range of racial, economic, educational, and political backgrounds. A more detailed report of methods and results will be shared on the Arizona Food Bank Network’s website in January 2023.
Under current United States Department of Agriculture (USDA) guidelines, Arizona schools participating in the National School Lunch Program and the School Breakfast Program are reimbursed for the meals they serve students through federal dollars and co-pays from student families. For this analysis, our goal was to estimate the cost to the State of Arizona if the breakfast and lunch co-pays for students that qualify for reduced-price meals were covered by the state.
We surveyed a diverse group of Arizona residents, including over 2,300 parents of school-age children and nearly 1,300 members of the school community, consisting of teachers, lunchroom staff, school administrators,and other school employees. Respondents represented a wide range of racial, economic, educational, and political backgrounds.
One approach to support such personalization is via self-experimentation using single-case designs. ‘Hack Your Health’ is a tool that guides individuals through an 18-day self-experiment to test if an intervention they choose (e.g., meditation, gratitude journaling) improves their own psychological well-being (e.g., stress, happiness), whether it fits in their routine, and whether they enjoy it.
The purpose of this work was to conduct a formative evaluation of Hack Your Health to examine user burden, adherence, and to evaluate its usefulness in supporting decision-making about a health intervention. A mixed-methods approach was used, and two versions of the tool were tested via two waves of participants (Wave 1, N=20; Wave 2, N=8). Participants completed their self-experiments and provided feedback via follow-up surveys (n=26) and interviews (n=20).
Findings indicated that the tool had high usability and low burden overall. Average survey completion rate was 91%, and compliance to protocol was 72%. Overall, participants found the experience useful to test if their chosen intervention helped them. However, there were discrepancies between participants’ intuition about intervention effect and results from analyses. Participants often relied on intuition/lived experience over results for decision-making. This suggested that the usefulness of Hack Your Health in its current form might be through the structure, accountability, and means for self-reflection it provided rather than the specific experimental design/results. Additionally, situations where performing interventions within a rigorous/restrictive experimental set-up may not be appropriate (e.g., when goal is to assess intervention enjoyment) were uncovered. Plausible design implications include: longer experimental and phase durations, accounting for non-compliance, missingness, and proximal/acute effects, and exploring strategies to complement quantitative data with participants’ lived experiences with interventions to effectively support decision-making. Future work should explore ways to balance scientific rigor with participants’ needs for such decision-making.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.