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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Smartphones dependency risk analysis using machine-learning predictive models.

Claudia Fernanda Giraldo-Jiménez1,2, Javier Gaviria-Chavarro2, Milton Sarria-Paja3

  • 1Department of Health, Universidad Santiago de Cali, Cali, Colombia.

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|December 31, 2022
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Summary
This summary is machine-generated.

This study developed a machine learning model to predict smartphone dependency in young adults using self-reported data. The model achieved 77% accuracy, highlighting the potential of data-driven approaches for identifying this growing addiction.

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Area of Science:

  • Digital Health
  • Psychology
  • Computer Science

Background:

  • Extensive technology use, particularly smartphones, contributes to sedentary lifestyles and new behavioral addictions.
  • Smartphone dependency is a growing concern, disproportionately affecting young populations and potentially leading to adverse mental and physical health outcomes.
  • Current assessment of smartphone dependency relies on subjective self-reports and expert opinions, necessitating more objective diagnostic tools.

Purpose of the Study:

  • To propose and evaluate a data-driven prediction model for smartphone dependency using machine learning techniques.
  • To assess the accuracy of various machine learning classifiers in predicting smartphone dependency.
  • To explore the utility of self-reported information in identifying individuals with smartphone dependency.

Main Methods:

  • An analytical retrospective case-control study was conducted with 1228 university students in Cali, Colombia.
  • Machine learning classification methods, including Random Forest, Logistic Regression, and Support Vector Machines, were applied.
  • Stratified k-fold cross-validation was employed to estimate prediction accuracy for smartphone dependency, musculoskeletal symptoms, and risk factors.

Main Results:

  • Random Forest, Logistic Regression, and Support Vector Machine classifiers demonstrated the highest prediction accuracy, ranging from 76% to 77%, for smartphone dependency.
  • The study confirmed that self-reported information can provide valuable insights for accurately predicting smartphone dependency.
  • The findings underscore the effectiveness of machine learning models in identifying potential cases of smartphone addiction.

Conclusions:

  • Self-reported data, when analyzed with machine learning, can effectively predict smartphone dependency.
  • The developed prediction model offers a promising approach for early identification and intervention of smartphone addiction.
  • Future research should incorporate objective measures to further enhance prediction accuracy and mitigate the negative impacts of smartphone dependency.