Algorithm-associated digital addiction among older adults: mechanisms and public health implications for healthy aging

Objective To examine how algorithmic inducement mechanisms are associated with digital addiction among Chinese adults aged 60 and above. Methods A cross-sectional survey was conducted among older users of major Chinese digital platforms. After data screening, 367 valid questionnaires were analyzed. Digital addiction was measured using the Mobile Phone Addiction Index (MPAI), while a self-developed 16-item scale assessed four algorithmic inducement dimensions: preferential incentives–profit-seeking psychological induction, interactive incentives–emotional compensation induction, stage goals–feedback effect induction, and customized recommendations–exploratory psychological induction. Multiple linear regressions were performed, controlling for age, living arrangement, and daily smartphone use duration. Results Digital addiction was widespread, within the sample, with 77.11% of participants scoring in the moderate or higher range. All four algorithmic dimensions were positively associated with digital addiction ( p < 0.01). Interactive incentives showed the strongest association ( β = 0.343), followed by preferential incentives ( β = 0.227); stage goals ( β = 0.160) and customized recommendations ( β = 0.163) were smaller yet significant. The model explained 79.1% of the variance ( R 2 = 0.791). Conclusion Algorithmic inducements—economic, social–emotional, task-feedback, and exploratory mechanisms—are jointly associated with a shift in older adults’ digital use from instrumental participation to immersive dependence. The study introduces an analytical framework, “algorithmic drive–psychological compensation–structural constraint–behavioral reinforcement,” and calls for coordinated algorithm governance and digital literacy initiatives to promote healthy aging in the digital era.