ArticleNpj mental health research2024
Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Digital Phenotyping of Lifestyle Profiles and Mental Well-Being in German Adults: Prospective Longitudinal Cohort Study.Journal of medical Internet research · 2026Article
- Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data.JMIR formative research · 2026Observational
- Emotion-Processing Biases and Neural Connectivity in Remitted Major Depressive Disorder among Adolescents.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- A multilevel meta-analysis of passive smartphone sensing of adolescent mental health.NPJ digital medicine · 2026Article
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- Predicting Emotional Exhaustion with Multimodal Sensor Data During Return-to-Work Trajectories: A 6-Month Longitudinal Study.Journal of occupational rehabilitation · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Article
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- The Digital Mental Health Study assessed feasibility of large-scale digital sensing for depression and anxiety.npj digital public health · 2026Article
- Differential associations of passively sensed behaviors with in-vivo depression symptoms.Journal of psychopathology and clinical science · 2025Article
- Smartphone use in a large US adult population: Temporal associations between objective measures of usage and mental well-being.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Early changes in passively sensed homestay predict depression symptom improvement during digital behavioral activation.Behaviour research and therapy · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- Passive Smartphone Sensors for Detecting Psychopathology.JAMA network open · 2025Article
- Observational
- Predicting the strength of next-day negative emotion states in body dysmorphic disorder using passive smartphone data: An intensive longitudinal assessment study.Internet interventions · 2025Article
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD.Scientific data · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal β = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (β = 0.198, p = 0.022) and proximal (β = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (β = -0.131, p = 0.035) but did not predict (distal β = 0.034, p = 0.577; medial β = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.
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Registered trials
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