Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Machine Learning Approach to Determine the Optimal Age for Total Hip Arthroplasty: When Is Risk for Adverse Outcomes Lowest?

The Journal of the American Academy of Orthopaedic Surgeons·2026
Same author

Development of lotus seed-based vegan yogurt: fermentation optimization, quality evaluation and analysis of key bioactive alkaloids.

Frontiers in nutrition·2026
Same author

[<sup>68</sup>Ga]Ga-FAPI-04 PET/CT for subtyping and prognostication of IgG4-related disease.

EJNMMI research·2026
Same author

68Ga-pentixafor PET/CT Indicates Inconsistent Response With Monoclonal Protein-based Response Criteria in Waldenström Macroglobulinemia/Lymphoplasmacytic Lymphoma Patients Treated With Bruton Tyrosine Kinase Inhibitors.

Clinical nuclear medicine·2026
Same author

Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis.

bioRxiv : the preprint server for biology·2026
Same author

Characterizing complex opioid use disorder care trajectories and outcomes following acute service utilization: A protocol for a population-based data linkage study.

PloS one·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.2K

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Hongzhe Zhang1, Jihui L Diaz1, Soohyun Kim1

  • 1Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to improve the accuracy of mobile health (mHealth) data by assessing device usage. The 2SpamH algorithm helps correct biases in passively sensed data for better digital health insights.

Keywords:
k-nearest neighbors algorithmmobile healthpassive sensingsmartphone

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.1K

Related Experiment Videos

Last Updated: Jun 7, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.2K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.1K

Area of Science:

  • Digital Health
  • Mobile Health (mHealth)
  • Biomedical Data Science

Background:

  • Mobile health (mHealth) technologies and wearable devices are increasingly used for collecting individualized behavioral data.
  • Variations in device usage across users and time can lead to underestimation and biases in passively sensed data.
  • Addressing data quality is crucial for accurate analysis in digital health applications.

Purpose of the Study:

  • To propose an unsupervised algorithm, 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH), to infer passive sensing data quality.
  • To address biases introduced by heterogeneous device usage in mHealth data.
  • To evaluate the algorithm's utility compared to existing methods.

Main Methods:

  • Development of the unsupervised 2SpamH algorithm.
  • Utilizing device usage variables to infer the quality of passively sensed mHealth data.
  • Conducting simulation studies and applying the algorithm to a real clinical dataset.

Main Results:

  • The proposed 2SpamH algorithm effectively infers the quality of passive sensing data from mobile devices.
  • Simulation studies demonstrate the utility of 2SpamH compared to existing methods.
  • Successful application of the algorithm to a real-world clinical dataset.

Conclusions:

  • The 2SpamH algorithm offers a robust solution for improving the quality of passively sensed mHealth data.
  • This method can mitigate biases caused by varying device usage patterns.
  • The findings support the integration of such algorithms for more reliable digital health research and applications.