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Empirical Mode Decomposition and Decision Tree in Sarcopenia

NCT05396404 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2022-09-08

No results posted yet for this study

Summary

Sarcopenia is quickly becoming a major global public health issue. Falls are the leading cause of mortality among the elderly, and they must be addressed. The investigators will use machine learning techniques such as empirical mode decomposition technology and decision tree algorithms to extract the characteristics and classification of sarcopenia in this retrospective study in order to offer clinically proven and effective interventional strategies to prevent, stabilize, and reverse sarcopenia.

Conditions

  • Sarcopenia
  • Fall
  • Gait, Unsteady
  • Balance; Distorted

Sponsors & Collaborators

  • Changhua Christian Hospital

    lead OTHER

Principal Investigators

  • TASEN WEI, MD · Changhua Christian Hospital

Eligibility

Min Age
40 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-03-01
Primary Completion
2024-01-31
Completion
2024-07-01

Countries

  • Taiwan

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05396404 on ClinicalTrials.gov