Personalised depression forecasting using mobile sensor data and ecological momentary assessment

Kathan A, Harrer M, Küster L, Triantafyllopoulos A, He X, Milling M, Gerczuk M, Yan T, Rajamani ST, Heber E, Grossmann I, Ebert DD, Schuller BW (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Frontiers in Digital Health Frontiers Media

Book Volume: 4

Article Number: 964582

DOI: 10.3389/fdgth.2022.964582

Abstract

Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients’ individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods: We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ((Formula presented.) patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models’ ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day. Results: In our experiments, we achieve a best mean-absolute-error (MAE) of (Formula presented.) (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ((Formula presented.)). For one day ahead-forecasting, we can improve the baseline of (Formula presented.) by (Formula presented.) to a MAE of (Formula presented.) using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level. Discussion: Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.

Authors with CRIS profile

Mathias Harrer Lehrstuhl für Klinische Psychologie und Psychotherapie

Involved external institutions

Universität Augsburg DE Germany (DE) HelloBetter GET.ON Institut für Online Gesundheitstrainings GmbH DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Kathan, A., Harrer, M., Küster, L., Triantafyllopoulos, A., He, X., Milling, M.,... Schuller, B.W. (2022). Personalised depression forecasting using mobile sensor data and ecological momentary assessment. Frontiers in Digital Health, 4. https://dx.doi.org/10.3389/fdgth.2022.964582

MLA:

Kathan, Alexander, et al. "Personalised depression forecasting using mobile sensor data and ecological momentary assessment." Frontiers in Digital Health 4 (2022).

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