RF Safe

Prediction of smartphone overdependence and analysis of its influencing factors among older adults based on machine learning.

Frontiers in psychology · 2026 · Unclear · Low evidence

← Research stream
UnclearLow evidencePaper 4330

The XGBoost machine learning classifier predicted smartphone overdependence among older adults with high accuracy (0.925). Key influencing factors included demographic characteristics, time spent on smartphone use, awareness of overdependence, and content use…

Study snapshot

PublicationFrontiers in psychology · 2026
Study typecross_sectional
Reported directionUnclear
Evidence ratingLow

Abstract

BACKGROUND: With the widespread use of smartphones among middle-aged and older adults, the risks associated with excessive use and dependence on smartphones have become increasingly apparent. This study aims to identify and predict the risk factors for smartphone overdependence among older adults in South Korea, utilizing machine learning methods to construct predictive models. METHODS: We utilized panel data from the "2023 Smartphone Overdependence Survey" provided by the National Information Society Agency (NIA) of South Korea. This study specifically focuses on the older adult population aged 60 and above, identifying key factors influencing their smartphone overdependence. A variety of machine learning-based binary classifiers were evaluated, including XGBoost, SVM, LR, KNN, DT, and NB. Their predictive accuracy and performance were compared comprehensively. Model performance was assessed using multiple metrics, including confusion matrix, accuracy, precision, recall, F1 score, and AUC. RESULTS: The XGBoost classifier performed the best in predicting smartphone overdependence among older adults, with an accuracy of 0.925. Through feature importance analysis, we found that demographic characteristics, time composition of smartphone use, awareness of smartphone overdependence problem, and content of smartphone use were the main influencing factors in predicting smartphone overdependence among older adults. CONCLUSION: Artificial intelligence algorithms have the potential for predictive and explanatory capabilities, identifying the risk of smartphone overdependence among older adults and the associated risk factors. This has significant theoretical and practical implications for understanding and addressing this issue.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typecross_sectional
Effect directionunclear
Populationolder adults aged 60 and above in South Korea
Sample sizeNot supplied
Exposureexcessive use
Evidence strengthLowConfidence: 30% · Peer reviewed: yes

Main findings

The XGBoost machine learning classifier predicted smartphone overdependence among older adults with high accuracy (0.925). Key influencing factors included demographic characteristics, time spent on smartphone use, awareness of overdependence, and content used on smartphones.

Outcomes measured

  • smartphone overdependence

Limitations

  • No sample size reported
  • Cross-sectional design limits causal inference
  • Study limited to older adults in South Korea
View raw extracted JSON
{
  "study_type": "cross_sectional",
  "exposure": {
    "band": null,
    "source": "smartphone",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": "excessive use"
  },
  "population": "older adults aged 60 and above in South Korea",
  "sample_size": null,
  "outcomes": [
    "smartphone overdependence"
  ],
  "main_findings": "The XGBoost machine learning classifier predicted smartphone overdependence among older adults with high accuracy (0.925). Key influencing factors included demographic characteristics, time spent on smartphone use, awareness of overdependence, and content used on smartphones.",
  "effect_direction": "unclear",
  "limitations": [
    "No sample size reported",
    "Cross-sectional design limits causal inference",
    "Study limited to older adults in South Korea"
  ],
  "evidence_strength": "low",
  "confidence": 0.3,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "smartphone overdependence",
    "older adults",
    "machine learning",
    "XGBoost",
    "South Korea"
  ],
  "suggested_hubs": []
}
AI can be wrong. Always verify against the paper.

AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.3389/fpsyg.2026.1704618
PubMed: 41675404

Read the original paper →

Related papers

← Back to the Research Library