A cross-sectional analysis of 2,275 community-dwelling adults aged 60 and older in Guangzhou applied machine learning to characterize smartphone addiction risk, which has been largely overlooked in later life. Rather than a uniform pattern, the data identified distinct subgroups with differing risk profiles, challenging the assumption that older adults respond to digital technology as a single population. The findings support individualized rather than blanket approaches to screen behavior in aging populations.
Key Points
- Smartphone addiction risk assessed in 2,275 adults aged 60-plus
- Machine learning identified distinct risk subgroups, not uniform patterns
- Older adults require individualized digital behavior assessment, not blanket guidance
Longevity Analysis
Digital overuse in later life competes directly with sleep, movement, and in-person connection — the inputs that sustain cognitive reserve and emotional regulation across the final decades. Identifying who is actually at risk, rather than assuming age confers either immunity or uniform vulnerability, allows interference to be removed from the specific individuals whose attention and arousal patterns are being disrupted. Prolonged screen engagement alters evening light exposure, postural load, and baseline nervous system activation, making it a modifiable variable with downstream effects on restorative sleep and daytime stability.
Original published by SAGE Research on Aging, by Sheng Chen, Yue Song, Chien-Chung Huang1School of Public Administration, Guangdong University of Foreign Studies, Guangzhou, China2School of Social Work, Rutgers University, New Brunswick, NJ, USA.