Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data

Major Depressive Disorder (MDD) is a common mental health condition marked by persistent low mood, reduced motivation, and low energy. Persons with MDD often experience large fluctuations in their symptoms over hours and days, which can offer valuable clinical insights, highlighting potential targets for treatment and intervention. Yet, it remains unclear how accurately these acute symptom changes can be predicted. In this preregistered study, we examined whether deep learning models could predict short-term fluctuations in depressive symptoms among individuals diagnosed with MDD. Passively-collected accelerometry data, a ubiquitous, privacy-preserving data stream with empirical ties to MDD symptomatology, was used to predict instability in depressive symptoms. Our sample contains clinically-depressed individuals per a structured clinical interview ( N = 179, Mean Age = 38.2 ± 10.0, Women = 81.6%). Participants completed depressive symptom assessments three times daily for 90 days, resulting in an average of 214.49 EMA questionnaires (median: 237, min: 77, max: 270, SD: 54.27) per person. Although our results suggest a small-moderate correspondence between the predictions and observed outcomes of passively-collected movement alone in predicting future high/low depression instability (AUC Test = 0.59 ± 0.06, Sensitivity Test = 0.56 ± 0.21, Specificity Test = 0.59 ± 0.23), our model performed worse than a random intercept only baseline model (AUC Test = 0.83) and predicted intraindividual variability of instability in depressive symptoms below chance (AUC Test = 0.46 ± 0.21). Thus, there is a need to improve predictive performance to enhance clinical utility. This may require the inclusion of additional passive sensing modalities in future research.