ArticleJMIR aging2026
Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data.
Article in JMIR aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Health correlates and health-related outcomes associated with smartphone-related addictive behaviors in older adults: a scoping review.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The increasing use of smartphones among older adults offers new opportunities for social connection but may also pose risks associated with adverse mental health outcomes, including depression. Objective: This study examined the relationship between smartphone use and depression among older adults in Guangzhou, China, to identify key predictors and complex configurations associated with depression. Methods: Using a hybrid analytic approach applied to survey data from 2585 older adults in Guangzhou, machine learning methods first identified the strongest predictors of depression. Subsequent fuzzy-set qualitative comparative analysis delineated distinct configurations associated with depression. Results: The analysis identified high levels of smartphone addiction and a low preference for interactive use as central configurations associated with depression. These patterns appeared to operate through 2 distinct mechanisms: structural exclusion related to resource limitations and agentic maladaptation associated with relational deficiencies. This underlying vulnerability, in combination with specific sociodemographic factors, delineated distinct depression-related typologies. High-risk profiles included unmarried men with limited educational attainment and socially withdrawn individuals with greater socioeconomic resources. Conclusions: Problematic smartphone use, particularly when it displaces social interaction, is significantly associated with depression among older adults. Interventions should therefore promote healthier interactive digital behaviors and provide targeted support for high-risk groups.
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Registered trials
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