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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Mar 13, 2022
Date Accepted: Oct 27, 2022

The final, peer-reviewed published version of this preprint can be found here:

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

Currey D, Torous J

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

JMIR Res Protoc 2022;11(11):e37954

DOI: 10.2196/37954

PMID: 36445745

PMCID: 9748794

Digital phenotyping data to predict symptom improvement and app personalization: Protocol for a prospective study

  • Danielle Currey; 
  • John Torous

ABSTRACT

Background:

Smartphone apps offering surveys and access to sensors are increasingly leveraged to collect data to provide insight into clinical conditions. As the mental health crisis in college students continues, apps provide a practical tool for students. Yet, uptake and engagement have remained limited.

Objective:

In this protocol, we present a study design to explore engagement with mental health apps in college students through the Technology Acceptance Model (TAM) as a theoretical framework. There are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students prospectively test this model on a new student cohort. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes towards the app.

Methods:

We aim to recruit at least 100 students into the study. All data will be collected via the mindLAMP app including both survey (active) and sensor (passive) data. The study duration is 30 days. This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform on the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement.

Results:

Results will be shared in a following publication.

Conclusions:

Results of this study will help inform how digital phenotyping methods can be used to advance smartphone-based adaptive interventions for college students as well as gain insights around app engagement in this population


 Citation

Please cite as:

Currey D, Torous J

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

JMIR Res Protoc 2022;11(11):e37954

DOI: 10.2196/37954

PMID: 36445745

PMCID: 9748794

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