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Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

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References

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2018Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing339 cited

2021Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data260 cited

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2023A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach19 cited
2024Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment18 cited
2024Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study10 cited
2023Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices9 cited
2025Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea9 cited
2025The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review7 cited
2024Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement5 cited