ArticleJMIR mHealth and uHealth2021
Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- Investigating mechanisms and monitoring outcomes in functional neurological disorder: the unrealized potential of digital remote monitoring technologies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Sleep and Activity Patterns in Depression From Wearable Data: Unsupervised Clustering Study.Journal of medical Internet research · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Longitudinal course is an essential dimension in psychiatric classification.Journal of psychiatry & neuroscience : JPN · 2026Article
- ADHD remote technology and ADHD transition: predicting and preventing negative outcomes (ART-transition) - an adolescent prospective cohort study protocol.BMC psychiatry · 2025Article
- Digital Measurement of Subjective Experiences in Alzheimer Disease and Related Dementias (AD/ADRD).JMIR aging · 2025Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protocol of a one-year observational trial.Contemporary clinical trials communications · 2025Article
- Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study.Journal of medical Internet research · 2025Observational
- Assessing seasonal and weather effects on depression and physical activity using mobile health data.Npj mental health research · 2025Article
- Identifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study.JMIR formative research · 2025Observational
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- Real-time biopsychosocial antecedents and correlates of functional neurological symptoms in daily life: A pilot remote monitoring technology study.Psychiatry research · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.JMIR mental health · 2024Article
- Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
backgroundResearch in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.
objectiveThis study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).
methodsThe data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.
resultsA number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R
conclusionsOur statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
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