ArticleBehaviour research and therapy2023
Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms.
Article in Behaviour research and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sensor-Based Technologies for the Detection of Unwanted Loneliness in Older Adults: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 2026Article
- Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Psychological crisis detection based on behavioral data: a new approach to non-contact measurement.BMC psychology · 2025Article
- Ambulatory physiological state dynamics predict proximal behavioral markers of affect regulation in everyday life.Journal of psychopathology and clinical science · 2025Article
- Ambulatory assessment to advance the science of nondrug reward in addiction and recovery.Addiction research & theory · 2025Article
- Digital health monitoring for adults with treatment-resistant depression: Observational feasibility study protocol.PloS one · 2025Article
- Prescription Digital Therapeutics: An Emerging Treatment Option for Negative Symptoms in Schizophrenia.Biological psychiatry · 2024Review
- Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.JMIR public health and surveillance · 2024Article
- Ladders to better health? Identifying the cognitive pathways and barriers to value attainment in wearable health device usage.Digital healthArticle
- Detecting Longitudinal Trends between Passively Collected Phone Use and Anxiety among College Students.Digital biomarkersArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.