Funded Projects: 2017-2018

A Targeted Social Media-based Intervention for Young Monthers: Improving Child Outcomes by Developing Artifical Intelligence and Intervention to Support Adolescent Mothers from Lower SES Backgrounds

PI: Amanda Hampton Wray

Co-Investigators: Dar Meshi, Mi Zhang, Kelly Brittain, Kami Silk, Andrea Wittenborn, Kendal Holtrop, Jiliang Tang, Kendra Moyses, and Julie Chapin

Adolescent mothers from lower socioeconomic status (SES) backgrounds are at risk for social isolation, depression, and other mental health challenges, and importantly, maternal mental health wellness is critical for positive child outcomes. This project will improve outcomes for children from lower SES backgrounds by harnessing artificial Intelligence (AI) and social media to support their adolescent mothers. Specifically, we will develop and evaluate a social media-based intervention to provide adolescent mothers from lower SES backgrounds with accessible mental health and parenting services. Our intervention will capitalize on mobile technology and AI to: a) Identify adolescent mothers in need of support; b) Provide at-risk adolescent mothers with mental health and parenting skills education; and c) Build social groups/networks to provide ongoing social support. Our intervention will be an efficient and scalable tool providing critical services to adolescent mothers, resulting in improved quality of life for both mothers and their children.

a woman using a smart phone

Using Facebook and Participatory Learning in an Intergenerational Intervention to Prevent Obesity in Head Start Preschoolers

PI: Jiying Ling

Co-investigators/Contributors: Mi Zhang, Jean Kerver, Lorraine Robbins, Nanhua Zhang

Despite disparities in obesity by socioeconomic status (SES), preschoolers ages 3-5 from low-SES backgrounds have been underrepresented in obesity prevention research. The primary aim of the proposed project is to determine the efficacy of an innovative intergenerational intervention targeting preschoolers and their parents simultaneously. Using a cluster randomized controlled trial design, this intervention seeks to prevent obesity and improve healthy behaviors among low-SES Head Start parent-preschooler dyads. This research stands to make a significant public health impact to reduce the prevalence of overweight and obesity among low-SES vulnerable Head Start preschoolers, with high potential for intervention scalability among an underserved population of concern to public health leaders and practitioners.

pre-schoolers sitting in a classroom

Addressing an Emerging Epidemic: Human-Centered Design Approaches to Developing personalized mHealth Services to Support Diabetes Prevention and Management in Urban Kenya

PI: Susan Wyche

Co-investigators/Contributors: Jennifer Olson, Bree Holtz, Denise Hershey

As obesity increases and populations age, diabetes is a serious emerging disease in low and middle-income countries. The lack of information has led to ignorance of diabetes symptoms, causes and preventative behaviors. Research in developed countries suggests that personalized information can reduce new-onset and management of the disease. mHealth can facilitate this information flow. Mobile phones are pervasive in Kenya and have been used for patient education and self-management using text messaging. Applications tend to provide generic information and rarely take advantage of phones’ affordances such as using texts with voice for multi-way communications. In Kenya, mHealth must overcome barriers of limited digital literacy, low incomes, and a lack of trust in texts. There is a need for mHealth application designs to reduce barriers and produce individualized, context-aware information. Human-Centered Design (HCD) produces locally-tailored products but it has not yet been tested in mHealth applications in developing countries.

buildings and people along a street in Nairobi

Data-driven Personalized Memory Assistance to Persons with Mild Cognitive Impairment

PI: Subir Biswas

Co-investigator: Linda Keilman

The objective of this project is to develop a technology-driven solution for improving the Quality of Life (QoL) for individuals diagnosed with MCI and/or early stage dementia. The key approach is to use an Internet-connected wearable system that can provide Personalized Memory Assistance (PMA) at the right time and right place for person recognition, improving engagement and self-confidence, leading to maintaining function for as long as possible, captured through the evidence-based tools, Katz Activities of Daily Living (ADL) and Lawton Instrumental Activities of Daily Living (IADL).

hand of a young person holding the hand of an older person