Background: Individuals from low-income and racial/ethnic minority backgrounds have traditionally had less access to mobile health (mHealth) technologies, but there is evidence that this gap has been rapidly narrowing. Given the increase in access to mobile technologies recently seen in vulnerable populations, mHealth has been championed as a strategy for improving population health and reducing health disparities. However, members of low-income and racial/ethnic minority populations have had a limited role in the development and implementation of mHealth interventions designed to impact them. Methods: We used community-based participatory research (CBPR), a research approach that is frequently employed to help reach communities that are disproportionately affected by illness but are difficult to engage. Our community-academic collaboration, the East Harlem Partnership for Diabetes Prevention, sought to create a mobile technology platform that would allow adults in East Harlem, New York to improve their own health and promote the health of the broader community. As a first step, we developed and conducted a survey of community residents to better understand access to, usage of, and attitudes towards mobile technologies among diverse, low-income adults. We administered the cross-sectional survey to a convenience sample of adults who utilized a variety of community-based organizations in East Harlem. We examined frequencies for each survey item and then used chi-square tests (or Fisher’s exact tests) and multivariate logistic regression to evaluate relationships between these outcomes and sociodemographic factors. Results: We approached 154 people, of whom 104 (68%) agreed to participate. The majority of respondents were of Black and/or Hispanic/Latino descent with a mean age of 37 years. Our sample displayed a high percentage of smartphone ownership (82% of the participants reported that they owned a cell phone, and 88% of owners reported that their cell phone was a smartphone). We found lower rates of ownership among individuals who were older, self-identified as Latino, insured by Medicare, and had a household income of less than $30,000 per year. Multivariate logistic regression showed that after adjusting for age, gender and race, those with at least a high school education were seven times more likely to use health apps than those with less than a high school education (OR 6.8, 95% CI: 1.7–27.1). Participants expressed interest in health promoting apps that provide interactive, individualized diet, exercise and weight loss tools and offer information about local health resources and events. Conclusions: Despite some notable disparities, our study results suggest that the digital divide is narrowing in the East Harlem community with relatively high rates of smartphone ownership and use, even among individuals from low-income, low education backgrounds and those without health insurance. Based on study results, our partnership developed an app supporting healthy lifestyle and diabetes prevention tailored to the East Harlem community.

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10⁻⁵).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

There is a need for diabetes prevention efforts targeting vulnerable populations. Our community–academic partnership, the East Harlem Partnership for Diabetes Prevention, conducted a randomized controlled trial to study the impact of peer led diabetes prevention workshops on weight and diabetes risk among an economically and racially diverse population in East Harlem, New York. We recruited overweight/obese adults from more than 50 community sites and conducted oral glucose tolerance testing and completed other clinical assessments and a health and lifestyle survey. We randomized prediabetic participants to intervention or delayed intervention groups. Intervention participants attended eight 90-minute peer-led workshop sessions at community sites. Participants in both groups returned for follow-up assessments 6 months after randomization. The main outcomes were the proportion of participants who achieved 5% weight loss, percentage weight loss, and change in the probability of developing diabetes over the next 7.5 years according to the San Antonio Diabetes Prediction Model. We enrolled 402 participants who were mainly female (85%), Latino (73%) or Black (23%), foreign born (64%), and non-English speaking (58%). At 6 months, the intervention group lost a greater percentage of their baseline weight, had significantly lower rise in HbA1c (glycated hemoglobin), decreased risk of diabetes, larger decreases in fat and fiber intake, improved confidence in nutrition label reading, and decrease in sedentary behavior as compared with the control group. Thus, in partnership with community stakeholders, we created an effective low-resource program that was less intensive than previously studied programs by incorporating strategies to engage and affect our priority population.

Objective. The study objective was to examine relationships between different body size measurements and asthma in ethnic minority children.Methods. We used data from a community-based study of 505 children aged 6-to-8 years old to study the association of percent body fat, fat distribution, and BMI percentile with asthma diagnosis. Poisson regression models were used to compute prevalence ratios (PRs) for sex-specific quintiles of the body fat measures on the main outcome of asthma.Results. When comparing the highest quintile of each body fat measure to the combined lowest two quintiles, higher body mass index percentile, percent body fat, and waist circumference all were associated with a higher likelihood of physician-diagnosed asthma (PR = 1.63 (95% CI 1.12–2.39), 1.50 (95% CI 1.02–2.21), and 1.56 (95% CI 1.04–2.34), resp.).Conclusions. This study found a significant association between increased body size and asthma diagnosis, regardless of the measurement examined.