Leveraging Large Language Models for Digital Phenotyping: Detecting Depressive State Changes for Patients with Depressive Episodes

Abstract

Digital phenotyping, which takes advantage of data continuously gathered from smartphones and wearable devices, offers promising avenues for real-time monitoring and mental health analysis. This approach holds promise for improving early detection and personalized care in mood disorders by enabling clinicians to proactively respond to significant changes before symptoms worsen. However, the complexity and heterogeneity of digital phenotyping data pose significant modeling challenges. Recent advances in large language models (LLMs) suggest their potential to generalize across diverse tasks with minimal labeled data, making them a promising alternative for analyzing data from digital phenotyping studies. However, the extent of usability of these methods for digital phenotyping studies is not yet well understood. In this study, we evaluate the potential of LLMs in analyzing digital phenotyping data to predict changes in depression severity among individuals experiencing major depressive episodes. We evaluate several in-context learning and fine-tuning strategies, and find that both few-shot prompted LLMs and fine-tuned models outperform traditional machine learning baselines trained on the same set of input features. Moreover, we compare two fine-tuning approaches (fine-tuning only the embedding layer versus parameter-efficient fine-tuning using QLoRA) and find that fine-tuning only the embedding layer significantly improves performance compared to QLoRA fine-tuning. These results highlight the capability of LLMs to process and integrate heterogeneous behavioral data, promising their application for digital phenotyping and mental health research. While our findings highlight the potential in using LLMs in mental health monitoring, their black-box nature and risk of replicating data biases highlight the need for clinical oversight and validation in real-world practice.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Ethical Committee of the Helsinki and Uusimaa Health District (12.09.2018; HUS/2337/2018). The project was granted a research permit by the Department of Psychiatry at the Helsinki University Hospital.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data Availability

The data underlying this study are not publicly accessible because of their sensitive nature, and the limitations specified in our research permit. Data access is restricted to members of the research consortium and cannot be provided to researchers outside the consortium.

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