JMIR Publications

ABSTRACT

Background:

Preventive campaigns for older adults must decide how to allocate limited resources across media channels. However, these channel allocation and budget decisions rarely use explicit criteria for distributional equity or digital health strategic planning. As a result, health systems may optimize average uptake while leaving large gaps across socioeconomic groups and media-use profiles.

Objective:

This study aimed to develop and apply a data-driven agent-based model as a strategic planning tool for older-adult preventive campaigns, comparing channel allocation, personalization, and loss framing options under explicit budget and equity constraints.

Methods:

We built an agent-based simulation calibrated to national survey data on influenza vaccination and routine health screening among older adults in South Korea. Fifteen prespecified campaign scenarios varied channel allocation across television (TV), digital, and print; total exposure budgets; two equity-focused personalization strategies; and graded loss framing. Primary outcomes were final adoption and time to adoption. Equity outcomes included the minimum class-level adoption and the 90–10 gap across latent classes. Each scenario was simulated over 12 monthly steps with 100 Monte Carlo replications. We also compared scenario portfolios using logistic and clipped-linear link functions and varied the balance of media versus social reinforcement weights, the social reinforcement threshold, and network realizations in sensitivity analyses.

Results:

TV-only and high-budget strategies produced some of the highest mean adoption rates for both vaccination and screening but often failed to meet equity guardrails for minimum class coverage and between-class gaps. In contrast, personalization strategies that modestly reweighted exposure toward the lowest-uptake class or assigned class-tailored channel portfolios maintained or improved mean adoption. These strategies also substantially raised minimum class-level coverage and narrowed disparities. When efficiency and distributional equity were considered jointly, these personalized portfolios emerged as the most attractive options under fixed budget constraints. Loss framing acted as a secondary tuning lever: within the tested range, stronger loss framing yielded small, monotonic gains in adoption and shorter time to adoption without worsening equity metrics. Scenario rankings were stable across sensitivity analyses, suggesting that the main patterns reflected underlying diffusion dynamics rather than any single modeling choice.

Conclusions:

This agent-based simulation shows how ex ante planning for preventive campaigns can move beyond intuition by comparing channel allocation and personalization options under explicit equity and budget criteria. For campaigns targeting older adults, modest equity-oriented personalization of TV and digital exposure improved or preserved mean uptake. It also consistently improved distributional equity, whereas diversified channel mixes without personalization were less efficient and less equitable. These findings support integrating equity guardrails and channel-allocation guardrails into early-stage campaign design and prioritizing targeted personalization over simple channel diversification. Future work should validate these patterns in other populations and health systems and link simulated diffusion trajectories with observed exposure and engagement in real-world digital and traditional-media campaigns.