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

Cancer Survival Analysis01:21

Cancer Survival Analysis

308
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
308
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.8K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.8K
Actuarial Approach01:20

Actuarial Approach

44
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
44
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

92
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
92
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

57
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
57

You might also read

Related Articles

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

Sort by
Same author

Validation of the Formulas for Mechanical Power in Children and Proposal of the Concept of "Effective Mechanical Power".

Journal of clinical medicine·2026
Same author

Synergy of Information in Multimodal Internet of Things Systems-Discovering the Impact of Daily Behaviour Routines on Physical Activity Level.

Sensors (Basel, Switzerland)·2025
Same author

Mapping of care pathways in pediatric and adult palliative care in Spain: A case study.

Palliative & supportive care·2025
Same author

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

Journal of medical Internet research·2025
Same author

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach.

Biomedicines·2024
Same author

A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care.

Bioengineering (Basel, Switzerland)·2024
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: May 14, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.4K

Personalised Risk Modelling for Older Adult Cancer Survivors: Combining Wearable Data and Self-Reported Measures to

Zoe Valero-Ramon1, Gema Ibanez-Sanchez1, Antonio Martinez-Millana1

  • 1ITACA-SABIEN, Universitat Politècnica de València, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study integrates wearable device data with patient-reported outcomes to create dynamic risk models for older adult cancer survivors. Findings show activity and sleep patterns impact vulnerability and anxiety, improving personalized cancer care.

Keywords:
dynamic risk modelsolder adult cancer patientsprocess miningtime-varying riskswearable devices

More Related Videos

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

8.1K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K

Related Experiment Videos

Last Updated: May 14, 2025

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

2.4K
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

8.1K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K

Area of Science:

  • Digital Health
  • Oncology
  • Geriatric Medicine

Background:

  • Electronic health records (EHRs) lack daily life context for remote patient monitoring.
  • Wearable devices offer objective data but need integration with patient-reported outcomes.
  • Older adult cancer survivors require personalized care considering evolving risk factors.

Purpose of the Study:

  • To develop dynamic risk models for vulnerability and anxiety in older adult cancer survivors.
  • To integrate wearable sensor data with self-reported outcomes for personalized cancer care.
  • To analyze the influence of activity and sleep patterns on patient-reported vulnerability and anxiety.

Main Methods:

  • Utilized process mining techniques to analyze real-world data from wearable devices and self-reported outcomes.
  • Combined objective data from wearables with subjective patient data (vulnerability, anxiety, depression).
  • Developed two dynamic risk models in collaboration with healthcare professionals using data from the LifeChamps study.

Main Results:

  • Prolonged sedentary activity correlated with a higher risk of vulnerability.
  • Highly dynamic sleep patterns were associated with increased reports of anxiety and depression.
  • Prostate-metastatic patients exhibited a higher risk of vulnerability than other cancer types.

Conclusions:

  • Wearable sensors and AI can enhance the analysis of dynamic risk factors in cancer care.
  • Personalized risk models integrating wearable and self-reported data are crucial for evolving patient needs.
  • Understanding the interplay between lifestyle patterns and psychological outcomes improves supportive cancer care.