Sun Sijie, Qi Na, Li Haoran, Xiao Liang
Department of Philosophy, Autonomous University of Barcelona, Barcelona, Spain.
Department of Design Graduate Schools, Sungkyunkwan University, Seoul, Republic of Korea.
Front Psychol. 2025 Jun 17;16:1540201. doi: 10.3389/fpsyg.2025.1540201. eCollection 2025.
Digitalization and aging are two defining characteristics of contemporary social transformation. As smart integrated devices become increasingly embedded in everyday life, understanding how elderly individuals interact with these technologies is essential for promoting digital inclusion and social integration. Although previous research has examined digital engagement among older adults, the specific behavioral and contextual factors that shape their usage of smart devices in a digital society remain underexplored. This study seeks to clarify how these factors operate and how they may be leveraged to support the well-being of the aging population.
This study utilized data from the 2020 China Longitudinal Aging Social Survey (CLASS), focusing on elderly individuals living in urban areas. A combination of logistic regression analysis, Lasso regression, and robustness tests was employed to identify the key predictors of smart integrated device usage. The analysis examined a range of variables including demographic characteristics, health status, education, income, internet usage experience, and family structure. Comparisons were made across groups to assess how these factors influence usage behavior, and Lasso regression was used to identify the most robust predictors.
The analysis revealed that elderly individuals are more likely to use smart devices if they are male, older, married, in poorer health, more highly educated, have lower income, have fewer children, and have previous experience using the internet. Among these, internet usage experience emerged as the most significant and consistent predictor across all models, as identified by the Lasso regression. Furthermore, the purposes for which the elderly use the internet, such as communication, information, or entertainment. These patterns were found to be stable even after controlling for potential confounding variables.
The findings challenge traditional assumptions about fixed sensory or cognitive hierarchies in aging populations and instead suggest that smart device usage among the elderly is shaped by a dynamic interplay of motivational, ability-based, and environmental factors. Drawing on Behavioral Design Theory, the study interprets these patterns through three lenses: motivation (e.g., health monitoring and social interaction), ability (e.g., physical and cognitive usability of devices), and triggers (e.g., technical and emotional support from family members). Under this framework, digital engagement appears to be highly context-dependent, with adaptive resource allocation and social support playing a crucial role in determining whether and how elderly individuals use smart technologies. The results emphasize the importance of designing inclusive digital environments and policies that respond to the nuanced needs and experiences of the aging population.
数字化和老龄化是当代社会转型的两个决定性特征。随着智能集成设备越来越融入日常生活，了解老年人如何与这些技术互动对于促进数字包容和社会融合至关重要。尽管先前的研究已经考察了老年人的数字参与情况，但在数字社会中塑造他们对智能设备使用的具体行为和背景因素仍未得到充分探索。本研究旨在阐明这些因素如何发挥作用，以及如何利用它们来支持老年人口的福祉。
本研究利用了2020年中国老年社会追踪调查（CLASS）的数据，重点关注居住在城市地区的老年人。采用逻辑回归分析、套索回归和稳健性检验相结合的方法来确定智能集成设备使用的关键预测因素。分析考察了一系列变量，包括人口特征、健康状况、教育程度、收入、互联网使用经验和家庭结构。对不同群体进行比较，以评估这些因素如何影响使用行为，并使用套索回归来确定最稳健的预测因素。
分析表明，如果老年人是男性、年龄较大、已婚、健康状况较差、受教育程度较高、收入较低、子女较少且有过互联网使用经验，那么他们更有可能使用智能设备。其中，互联网使用经验在所有模型中都是最显著且一致的预测因素，这是由套索回归确定的。此外，老年人使用互联网的目的，如通信、信息或娱乐。即使在控制了潜在的混杂变量之后，这些模式仍然稳定。
研究结果挑战了关于老年人群体中固定感官或认知层次结构的传统假设，相反，表明老年人对智能设备的使用是由动机、基于能力和环境因素的动态相互作用所塑造的。借鉴行为设计理论，本研究通过三个视角来解释这些模式：动机（如健康监测和社交互动）、能力（如设备的物理和认知可用性）和触发因素（如家庭成员的技术和情感支持）。在此框架下，数字参与似乎高度依赖于背景，适应性资源分配和社会支持在决定老年人是否以及如何使用智能技术方面发挥着关键作用。研究结果强调了设计包容性数字环境和政策以回应老年人群体细微需求和经验的重要性。
