ArticleJMIR mHealth and uHealth2018
Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04104347 (Does Metacognitive Training Improve Insight and Clinical Outcomes in Schizophrenia?), which is not on this map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Does Metacognitive Training Improve Insight and Clinical Outcomes in Schizophrenia?
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study.Journal of medical Internet research · 2021Trial
- Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data.Journal of medical Internet research · 2026Article
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- Exploring current smartphone-based cognitive assessments in schizophrenia and bipolar disorder.Schizophrenia research. Cognition · 2024Review
- Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study.JMIR formative research · 2023Article
- One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study.Journal of medical Internet research · 2023Article
- Automatic patient functionality assessment from multimodal data using deep learning techniques - Development and feasibility evaluation.Internet interventions · 2023Article
- Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study.JMIR formative research · 2023Article
- Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.JMIR mental health · 2021Article
- Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.Molecular psychiatry · 2021Article
- Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.JMIR mental health · 2021Article
- Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study.JMIR biomedical engineering · 2021Article
- Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study.Journal of medical Internet research · 2021Article
- Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.JMIR mHealth and uHealth · 2021Article
- Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring.Frontiers in digital health · 2021Review
- Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study.JMIR mHealth and uHealth · 2020Article
- Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia.BMC psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients' active participation. We designed a system to detect changes in the mobility patterns based on the smartphone's native sensors and advanced machine learning and signal processing techniques.
objectiveThe principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone's sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique.
methodsIn this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB
resultsThe principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone's native sensors data. Here, results from 5 patients' records are presented as a case series. The eB
conclusionsThe proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.