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Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study.

Danielle Currey1, John Torous1

  • 1School of Medicine, Case Western Reserve University, Cleveland, OH, United States.

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Summary

This study prospectively validates digital phenotyping algorithms for predicting mental health changes in college students. Personalized activity suggestions may increase engagement with mental health apps.

Keywords:
Technology Acceptance Modeladoptionanxietyappcollege studentdepressiondigital healthdigital phenotypedigital phenotypingengagementhealth appmHealthmental healthmobile healthsmartphoneuniversity studentyoung adult

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Area of Science:

  • Digital Health
  • Mental Health Technology
  • Computational Psychiatry

Background:

  • Smartphone data collection offers potential for personalized mental health support.
  • Accurate prediction of future mental health is a key challenge for current mental health apps.
  • Digital phenotyping can leverage sensor and survey data for mental health insights.

Purpose of the Study:

  • To explore college student engagement with mental health apps.
  • To assess the accuracy of digital phenotyping for predicting mental health changes.
  • To evaluate the impact of personalized activity suggestions on app engagement using the Technology Acceptance Model.

Main Methods:

  • A logistic regression model was fitted and prospectively tested on a new cohort of college students.
  • Digital phenotyping data was used to predict changes in mental health.
  • Users received data-driven activity suggestions every 4 days to assess impact on engagement and attitudes.

Main Results:

  • The study was completed in Spring 2022, with manuscript under review.
  • This research represents one of the first prospective validations of digital phenotyping algorithms.
  • Results will indicate the utility of digital phenotyping for adaptive interventions and engagement.

Conclusions:

  • Digital phenotyping data holds promise for tailoring interventions in mental health apps.
  • Prospective validation is crucial for the reliability of digital phenotyping algorithms.
  • Findings will inform strategies to enhance user engagement in digital mental health solutions.