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The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior.

Laura Simon1, Yannik Terhorst1, Caroline Cohrdes2

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Smartphone usage features show weak correlations with insomnia symptoms. Machine learning models using these features had low accuracy, suggesting they are insufficient for detecting insomnia. Further research is needed.

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Area of Science:

  • Digital phenotyping
  • Sleep science
  • Machine learning applications

Background:

  • Insomnia detection can be improved through innovative methods like digital phenotyping.
  • This study investigates smartphone usage features (SUF) for correlations with insomnia symptoms.

Purpose of the Study:

  • To explore correlations between SUF and insomnia symptoms.
  • To assess the predictive value of SUF for detecting insomnia symptoms using machine learning.

Main Methods:

  • An observational study analyzed smartphone usage data and Insomnia Severity Index (ISI) scores from 752 participants.
  • Correlation analyses and six machine learning algorithms were used to predict insomnia severity (ISI scores ≥15).

Main Results:

  • Small correlations were found between some SUF and insomnia symptoms.
  • Machine learning models demonstrated low sensitivity (0.05–0.27) and discrimination capacity (AUCs 0.57–0.58).
  • Random Forest and Naive Bayes were the best-performing algorithms but still showed limited predictive power.

Conclusions:

  • SUF, as measured, are insufficient for detecting insomnia symptoms due to weak correlations and low model discrimination.
  • Further research should explore intra-individual variations, subpopulations, or alternative smartphone sensors for improved detection.