OBJECTIVE: This study aimed to synthesize current literature on digital solutions for CVD prevention, identify the key components of effective digital interventions, and highlight critical research gaps to inform the development of sustainable strategies for CVD prevention.

METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a comprehensive search in Web of Science, Scopus, and PubMed to identify original English-language studies published between January 2000 and May 2024 that examined primary or secondary CVD prevention through digital solutions. The exclusion criteria included: telephone-only interventions, abstract-only publications, methodology-focused studies without primary data, studies without participants or specific groups, and studies with no follow-up period. The literature search used the string with terms like "digital health," "mHealth," "mobile health," "text message," "short message service," "SMS," "prevention," "prevent," "cardiovascular disease," "CVD," etc. Study bias was assessed using the RoB 2 (Cochrane Collaboration) and the ROBINS-I tool (Cochrane Collaboration). Data on prevention components, prevention types, study design, population, intervention, follow-up duration, personnel, and delivery settings were extracted.

RESULTS: A total of 2871 studies were identified through the search. After excluding ineligible studies, 30 studies remained, including 24 randomized controlled trials. The reviewed digital solutions for CVD prevention focused on baseline assessment (29/30, 97%), physical activity counseling (18/30, 60%), tobacco cessation (14/30, 47%), blood pressure management (13/30, 43%), and medication adherence (10/30, 33%). The technologies used were categorized into 3 types, smartphones and wearables (16/30, 53%), email and SMS communications (12/30, 40%), and websites or web portals (3/30, 10%). The majority of the study outcomes addressed blood pressure (14/30, 47%), exercise capacity (12/30, 40%), weight (12/30, 40%), and lipid profile (11/30, 37%), while fewer focused on nicotine dependence (9/30, 30%), medication use (8/30, 27%), quality of life (7/30, 23%), dietary habits (5/30, 17%), intervention adherence (4/30, 13%), waist circumference (4/30, 13%), and blood glucose levels (2/30, 7%).

OBJECTIVE: We aimed to investigate the correlation between social media and eHealth literacy in older individuals and answer four research questions: (1) What are the specific social media behaviors (including general use behaviors and health behaviors) of older adults on social media? (2) How do these behaviors impact their eHealth literacy? (3) How does eHealth literacy influence older adults' social media behaviors? and (4) What factors influence older adults' use of social media for health-related purposes?

METHODS: Using predetermined keywords and inclusion criteria, we searched Scopus, Web of Science, and PubMed databases for English-language journal articles published from 2000 to 2024, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles. The initial search identified 1591 publications, and after removing duplicates, 48.21% (767/1591) of publications remained. Ultimately, 1% (16/1591) of studies met the inclusion criteria. A research question-driven manual qualitative thematic analysis was conducted, guided by the categorization of social media use behaviors, the definition of eHealth literacy, and the social-ecological model to provide direction for coding and thematic analysis. In addition, attention was given to identifying unanticipated behaviors or phenomena during the coding process, and these were subsequently incorporated into the analytical framework.

RESULTS: The results indicated that older adults' general social media use behaviors are primarily characterized by social media preferences, with 2 subthemes identified. Their social media health behaviors revealed 5 main themes and 14 subthemes. Among the primary themes, health information behavior appeared most frequently (12/16, 75%), followed by self-management (8/16, 50%). Other themes included health decision-making (4/16, 25%), telemedicine (3/16, 19%), and health interventions (2/16, 13%). Cross-thematic analysis confirmed that older adults' social media use behaviors and their eHealth literacy had a reciprocal relationship. Finally, the study revealed that the use of social media to improve eHealth literacy among older adults was influenced by individual, interpersonal, institutional or organizational, and social factors.

METHODS: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.

RESULTS: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10(-4); OR, 1.04; 95% confidence interval (CI), 1.02-1.07] and rs77928427 (P = 1.86 × 10(-4); OR, 1.04; 95% CI, 1.02-1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r(2) ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor-binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.

CONCLUSION: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.

METHODS: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).

RESULTS: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.

CONCLUSION: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.