The Design, Usability, and Feasibility of a Family-Focused Diabetes Self-Care Support mHealth Intervention for Diverse, Low-Income Adults with Type 2 Diabetes.
Lindsay Satterwhite MayberryCynthia A BergKryseana J HarperChandra Y OsbornPublished in: Journal of diabetes research (2016)
Family members' helpful and harmful actions affect adherence to self-care and glycemic control among adults with type 2 diabetes (T2D) and low socioeconomic status. Few family interventions for adults with T2D address harmful actions or use text messages to reach family members. Through user-centered design and iterative usability/feasibility testing, we developed a mHealth intervention for disadvantaged adults with T2D called FAMS. FAMS delivers phone coaching to set self-care goals and improve patient participant's (PP) ability to identify and address family actions that support/impede self-care. PPs receive text message support and can choose to invite a support person (SP) to receive text messages. We recruited 19 adults with T2D from three Federally Qualified Health Centers to use FAMS for two weeks and complete a feedback interview. Coach-reported data captured coaching success, technical data captured user engagement, and PP/SP interviews captured the FAMS experience. PPs were predominantly African American, 83% had incomes <$35,000, and 26% were married. Most SPs (n = 7) were spouses/partners or adult children. PPs reported FAMS increased self-care and both PPs and SPs reported FAMS improved support for and communication about diabetes. FAMS is usable and feasible and appears to help patients manage self-care support, although some PPs may not have a SP.
Keyphrases
- glycemic control
- type diabetes
- african american
- electronic health record
- randomized controlled trial
- cardiovascular disease
- healthcare
- end stage renal disease
- mental health
- young adults
- health information
- smoking cessation
- ejection fraction
- chronic kidney disease
- weight loss
- skeletal muscle
- metabolic syndrome
- prognostic factors
- insulin resistance
- social media
- peritoneal dialysis
- risk assessment
- deep learning
- global health