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Accepted for/Published in: JMIR Aging

Date Submitted: Sep 23, 2025
Date Accepted: Feb 10, 2026

The final, peer-reviewed published version of this preprint can be found here:

Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data

Chen S, Song Y, Huang Cc

Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data

JMIR Aging 2026;9:e84703

DOI: 10.2196/84703

PMID: 41818479

Smartphone Addiction, Usage Preferences, and Depression Among Older Adults in the Digital Age: Machine Learning Analysis from Survey Study

  • Sheng Chen; 
  • Yue Song; 
  • Chien-chung Huang

ABSTRACT

Background:

The rapid proliferation of digital technologies, coupled with the accelerating pace of population aging, has led to older adults becoming increasingly prominent smartphone users. The proportion of individuals aged 60 and above in China rose from 14.9% in 2013 to 21.1% in 2023, signaling the country's transition towards a moderately aging society (Ministry of Civil Affairs, 2024a). Data from the Fifth National Survey on the Living Conditions of Urban and Rural Older Persons indicate that, as of 2021, 36.6% of older Chinese adults used smartphones, with usage rates nearing 50% among the younger-old cohort, aged 60–69 (Ministry of Civil Affairs, 2024b). According to the 56th Statistical Report on China's Internet Development released in July 2025, the internet penetration rate among the senior population in China reached 52.0%, with the number of internet users aged 60 and over totaling 161 million (China Internet Network Information Center, 2025). This trend reflects the growing digital engagement of older adults in contemporary society.

Objective:

This study explores the relationship between smartphone use and depression among older adults in Guangzhou, China, aiming to identify key predictors and complex pathways leading to this condition.

Methods:

The study uses a hybrid analytical approach on survey data from 2,585 older adults. First, machine learning models identified the strongest predictors of depression as smartphone addiction and low social participation. Subsequently, fuzzy-set Qualitative Comparative Analysis (fsQCA) revealed three distinct pathways to depression.

Results:

The analysis found a core condition across these pathways: the combination of high smartphone addiction and a low preference for interactive use. When this core vulnerability is coupled with socio-demographic factors, it forms distinct pathways to depression. Key high-risk profiles include unmarried men with low education and socially withdrawn individuals from high-resource backgrounds.

Conclusions:

The study concludes that problematic smartphone use is a significant risk factor for depression in older adults, particularly when it displaces real-world social engagement. Therefore, interventions should focus on cultivating healthy, interaction-centered digital habits and providing targeted support for high-risk groups. Clinical Trial: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Review Committee of School of Public Administration Guangdong University of Foreign Studies (Protocol #2024003).


 Citation

Please cite as:

Chen S, Song Y, Huang Cc

Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data

JMIR Aging 2026;9:e84703

DOI: 10.2196/84703

PMID: 41818479

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