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 (i) to 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 (ii) to 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 which uses publicly available data at the ZCTA level to uncover demographic environments using clustering analysis which is then used to guide digital health intervention using the Meta business platform. The SNI delivered five tailored organ donation related educational contents through Facebook to four 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 (beta=.2187;P<.001), with the highest effect on Cluster 1 (beta=.3683; P<.001) and the lowest effect on Cluster 4 (beta=.0936.;P=0.053). 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 was more likely to be urban, Hispanic, and African-American, with high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS The proposed SNI framework, with its adaptive content tuning 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. CLINICALTRIAL This study was approved by the Institutional Review Board (IRB) Office of University of California, Davis, US (1596733-2). The study was registered on ClinicalTrials.gov (NTC04850287).
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
Background: Evidence from longitudinal studies points to the syndromic continuum of dementia. Individuals with mild cognitive impairment (MCI) are at increased risk of progressing to dementia over time, as well as older adults with subjective cognitive decline (SCD). Objective: To assess the impact of treating reversible causes of dementia on the outcome of patients with cognitive decline. Methods: Data were collected between 2017 and 2020 (mean follow-up = 44.52 ±6.85 months) in primary health care in Patos de Minas, MG. Subjects were screened using the MMSE, Figure Memory Test, Verbal Fluency, Clock Drawing Test, Geriatric Depression Scale, Geriatric Anxiety Inventory, and the Functional Activities Questionnaire. Results: Of 15 patients with SCD, 26.7% progressed to MCI. Of 45 patients with MCI, 13.4% progressed to dementia, 4.4% died and 26.7% regressed to SCD. Of 31 individuals with dementia, 6.5% regressed to SCD, 22.6% regressed to MCI and 19.4% died. Clinical improvement can be explained by the treatment of reversible causes, such as hypothyroidism, hypovitaminosis B12, and mood and anxiety disorders. Conclusion: Two-thirds of people who meet the criteria for MCI do not convert to dementia during the follow-up. These results reinforce the need of adequate screening and treatment of reversible causes of dementia in the primary care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.