This trial is registered at ClinicalTrials.gov with clinical trial registration number HUM00081734. AbstractBackground: Patients' engagement in mobile health (m-health) interventions using interactive voice response (IVR) calls is less in low-and middle-income countries (LMICs) than in industrialized ones. We conducted a study to determine whether automated telephone feedback to informal caregivers (''CarePartners'') increased engagement in m-health support among diabetes and hypertension patients in Bolivia. Materials and Methods: Patients with diabetes and/or hypertension were identified through ambulatory clinics affiliated with four hospitals. All patients enrolled with a CarePartner. Patients were randomized to weekly IVR calls including self-management questions and self-care education either alone (''standard m-health'') or with automated feedback about health and selfcare needs sent to their CarePartner after each IVR call (''m-health+CP''). Results: The 72 participants included 39 with diabetes and 53 with hypertension, of whom 19 had £6 years of education. After 1,225 patient-weeks of attempted IVR assessments, the call completion rate was higher among patients randomized to m-health+CP compared with standard mhealth (62.0% versus 44.9%; p < 0.047). CarePartner feedback more than tripled call completion rates among indigenous patients and patients with low literacy (p < 0.001 for both). Mhealth+CP patients were more likely to report excellent health

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Peri-urban, but not urban, residence in Bolivia is associated with a higher risk of the nutritional double burden than rural areas. Understanding how heterogeneous urban environments influence nutrition outcomes could inform integrated policies that simultaneously address both undernutrition and obesity.

Food insecurity and limited healthy food access are complex public health issues and warrant multi-level evaluations. The purpose of this paper was to present the overall study design and baseline results of the multi-pronged evaluation of a healthy food access (i.e., Fresh for Less (FFL)) initiative in Central Texas. The 2018–2021 FRESH-Austin study was a natural experiment that utilized a cluster random sampling strategy to recruit three groups of participants (total n = 400): (1) customers at FFL assets, (2) residents that lived within 1.5 miles of an FFL asset, and (3) residents from a comparison community. Evaluation measures included annual cohort surveys, accelerometers and GPS devices, store-level audits, and built environment assessments. Data are being used to inform and validate an agent-based model (ABM) to predict food shopping and consumption behaviors. Sociodemographic factors and food shopping and consumption behaviors were similar across the three groups; however, customers recruited at FFL assets were lower income and had a higher prevalence of food insecurity. The baseline findings demonstrate the need for multi-level food access interventions, such as FFL, in low-income communities. In the future, ABM can be used as a cost-effective way to determine potential impacts of future large-scale food environment programs and policies.