A digital transformation and consumption patterns of the "New older adults" in China: a systematic review of technological adaptation and learning mechanisms
Abstract
China is experiencing a profound demographic shift which is mainly marked by rapid population aging. This systematic review explores the key domains, market segments, and technological innovations targeting China's new older adults population, employing thematic analysis to synthesize findings from diverse studies. The review identifies multifaceted market segmentation approaches, including demographic, geographic, psychographic, health, and economic channels, which highlights the heterogeneity of older adults consumers. Technological advancements span health and wellness, living environment safety, social inclusion, data infrastructure, and support services, providing benefits such as enhanced health monitoring, independent living, social connectivity, and resource optimization. But current research is constrained by narrow geographic coverage, limited representation of non-digital users, and reliance on theoretical or macro-level analyses, restricting understanding of real-world adoption and long-term outcomes, and future strategies should focus on user-centered technology design, differentiated market segmentation, inclusive digital literacy initiatives, and integrated, AI-enabled service delivery. Also, robust, multi-sector longitudinal studies are needed to evaluate adoption, effectiveness, and scalability of interventions, and this review provides actionable insights for policymakers, industry, and researchers to develop inclusive, efficient, and sustainable support…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.