Empirical Mode Decomposition and Decision Tree in Sarcopenia

NCT ID: NCT05396404

Last Updated: 2022-09-08

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-03-01
• Study Completion Date: 2024-07-01

Brief 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.

Detailed Description

Sarcopenia is becoming a severe global public health concern as the world's elderly population grows. Sarcopenia is characterized by muscular mass and strength loss, as well as impaired physical performance, and it is frequently connected with negative health outcomes such as falls. Falls are a primary cause of death in older individuals and must be addressed. Sarcopenia is currently diagnosed clinically using three primary technologies: imaging technology, precision medicine, and machine learning. In this study, the investigators will use previously collected data from nearly 200 community-dwelling subjects, including medical history, biochemistry, body composition, balance and gait, electromyography, and functional performance, to extract the characteristics and classification of sarcopenia using machine learning techniques such as empirical mode decomposition technology and decision tree algorithms. The investigators intend to offer clinically proven and effective interventional strategies to prevent, stabilize, and reverse sarcopenia.

Conditions

Sarcopenia; Fall; Gait, Unsteady; Balance; Distorted

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

observation

all subject data were retrieved from databank which is stored in the e-medical chart system.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• aged from 40 - 90
• DXA test performed
• blood sample tests were performed

Exclusion Criteria

• stroke history
• amputation
• cancer related disease

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Changhua Christian Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

TASEN WEI, MD

Role: PRINCIPAL_INVESTIGATOR

Changhua Christian Hospital

Locations

Changhua Christian Hospital

Changhua, , Taiwan

Countries

Taiwan

Other Identifiers

CCH IRB 211235

Identifier Type: -

Identifier Source: org_study_id