ArticleFrontiers in digital health2021
Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.
Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis 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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Responsible and innovative AI for mental health care: five priority themes.NPP - digital psychiatry and neuroscience · 2026Review
- Six decades of animal accelerometry: trends, applications, and future directions.Movement ecology · 2026Review
- Observational
- Usability, feasibility, and acceptability of a smartphone-supported behavioral activation intervention for patients with advanced cancer.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2026Article
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- Consumer Wearable Device Measures of Gait Cadence and Activity Fragmentation as Predictors of Survival Among Patients Undergoing Chemotherapy.JCO clinical cancer informatics · 2025Observational
- Nomophobia, Psychopathology, and Smartphone-Inferred Behaviors in Youth With Depression: Longitudinal Study.JMIR formative research · 2025Article
- Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.Journal of medical Internet research · 2025Observational
- An association of cognitive function with mobile metrics of community walking in older cancer survivors: A pilot study.Journal of geriatric oncology · 2025Article
- Improving the Science of Adolescent Social Media and Mental Health: Challenges and Opportunities of Smartphone-Based Mobile Sensing and Digital Phenotyping.Journal of technology in behavioral science · 2025Article
- Adolescents' Self-Regulation of Social Media Use During the Beginning of the COVID-19 Pandemic: An Idiographic Approach.Journal of technology in behavioral science · 2024Article
- Engagement With Daily Symptom Reporting, Passive Smartphone Sensing, and Wearable Device Data Collection During Chemotherapy: Longitudinal Observational Study.JMIR cancer · 2024Observational
- Article
- Associations between performance-based and patient-reported physical functioning and real-world mobile sensor metrics in older cancer survivors: A pilot study.Journal of geriatric oncology · 2024Article
- Automated pipeline for denoising, missing data processing, and feature extraction for signals acquired via wearable devices in multiple sclerosis and amyotrophic lateral sclerosis applications.Frontiers in digital health · 2024Article
- Adolescents' daily social media use and mood during the COVID-19 lockdown period.Current research in ecological and social psychology · 2024Article
- Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior.Future generations computer systems : FGCS · 2022Article
- Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating.JMIR mental health · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.
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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.