Exploring the Neuropsychiatric Sequalae of Perceived COVID-19 Exposure in College Students: A Pilot Digital Phenotyping Study

Designing smartphone-based cognitive assessments for schizophrenia: Perspectives from a multisite study

Introduction

Cognitive deficits represent a core symptom of schizophrenia and a principal contributor to illness disability, yet evaluating cognition in routine clinical settings is often not feasible as cognitive assessments take longer than a standard doctor's visit. Using smartphones to assess cognition in schizophrenia offers the advantages of convenience in that patients can complete assessments outside of the clinic, temporality in that longitudinal trends can be identified, and contextuality in that cognitive scores can be interpreted with other measures captured by the phone (e.g. sleep). The current study aims to design a battery of cognitive assessments corresponding to the MATRICs Consensus Battery of Cognition (MCCB), in partnership with people living with schizophrenia.

Methodology

Focus group discussions (FGDs) and interviews were conducted with people diagnosed with schizophrenia across three sites (Boston, Bhopal, and Bangalore) to help design, refine, and assess the proposed smartphone battery of cognitive tests on the mindLAMP app. Interviews were conducted between December 2023 and March 2024. Inductive thematic analysis was used to analyze data.

Results

Participants found the app and its proposed cognitive assessments to be acceptable, helpful, and easy to use. They particularly found the gamified nature of the cognitive tests to be appealing and engaging. However, they also proposed ways to further increase engagement by including more information about each cognitive test, more visual instructions, and more information about scoring. Across all sites, there were many similarities in themes.

Discussion & conclusion

People living with schizophrenia, from different sites in the US and India, appear interested in using smartphone apps to track their cognition. Thematic analysis reinforces the importance of feedback and data sharing, although this presents a challenge, given the novel nature of smartphone-based cognitive measures that have not yet been standardized or validated.

Digital Phenotyping Models of Symptom Improvement in College Mental Health: Generalizability Across Two Cohorts

Smartphones can be used to gain insight into mental health conditions through the collection of survey and sensor data. However, the external validity of this digital phenotyping data is still being explored, and there is a need to assess if predictive models derived from this data are generalizable. The first dataset (V1) of 632 college students was collected between December 2020 and May 2021. The second dataset (V2) was collected using the same app between November and December 2021 and included 66 students. Students in V1 could enroll in V2. The main difference between the V1 and V2 studies was that we focused on protocol methods in V2 to ensure digital phenotyping data had a lower degree of missing data than in the V1 dataset. We compared survey response counts and sensor data coverage across the two datasets. Additionally, we explored whether models trained to predict symptom survey improvement could generalize across datasets. Design changes in V2, such as a run-in period and data quality checks, resulted in significantly higher engagement and sensor data coverage. The best-performing model was able to predict a 50% change in mood with 28 days of data, and models were able to generalize across datasets. The similarities between the features in V1 and V2 suggest that our features are valid across time. In addition, models must be able to generalize to new populations to be used in practice, so our experiments provide an encouraging result toward the potential of personalized digital mental health care.

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

BACKGROUND

Smartphone apps that capture surveys and sensors are increasingly being leveraged to collect data on clinical conditions. In mental health, this data could be used to personalize psychiatric support offered by apps so that they are more effective and engaging. Yet today, few mental health apps offer this type of support, often because of challenges associated with accurately predicting users' actual future mental health.

OBJECTIVE

In this protocol, we present a study design to explore engagement with mental health apps in college students, using the Technology Acceptance Model as a theoretical framework, and assess the accuracy of predicting mental health changes using digital phenotyping data.

METHODS

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 and prospectively test this model on a new student cohort to assess its accuracy. 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 toward the app compared to those receiving no personalized recommendations.

RESULTS

The study was completed in the spring of 2022, and the manuscript is currently in review at JMIR Publications.

CONCLUSIONS

This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37954.