Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial

Abstract

Background Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities. Objective This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities. Methods This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation–related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. Results Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (β=.2187; P<.001), with the highest effect on cluster 1 (β=.3683; P<.001) and the lowest effect on cluster 4 (β=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries. Conclusions The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. Trial Registration ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287

Association of Neighborhood and Environmental Factors With Clinical Phenotypes and Outcomes in Heart Failure With Preserved Ejection Fraction

“…2 Defined by the Centers for Disease Control and Prevention and the World Health Organization, SDoH refers to the conditions in the environments where people are born, live, learn, work, play, worship, and age, influencing health outcomes and quality-of-life risks. SDoH encompasses 5 domains: (1) Education Access and Quality, (2) Economic Stability, (3) Social and Community Context, (4) Health Care Access and Quality, (5) Neighborhood and Built Environment.…”

“…Our community clustering algorithm 4,5 used 24 sociodemographic and socioeconomic variables previously described, 5 across 5 main domains of SDoH, at ZIP code tabulation areas (ZCTA) level (from the American Community Survey, https://data.census.gov/cedsci/ [accessed 7 March, 2022]). Prominent variables were education level, access to health care, household income, and employment.…”