Clinical trial NCT05396404: Empirical Mode Decomposition and Decision Tree in Sarcopenia
Official title: Using Empirical Mode Decomposition and Decision Tree to Extract the Balance and Gait Features and Classification in Sarcopenia
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Sarcopenia
Fall
Gait, Unsteady
Balance; Distorted
Keywords: empirical mode decomposition
machine learning
decision tree
Lead sponsor: Changhua Christian Hospital
Sponsor class: OTHER
First posted: 2022-05-31
Enrollment: 200
Sex: ALL
Minimum age (years): 40
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The subject's data were collected from the e-medical chart system.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* stroke history
* amputation
* cancer related disease

Primary outcomes:
measure: center of pressure (COP)
timeFrame: baseline: subject was enrolled
measure: walking speed
timeFrame: baseline: subject was enrolled
measure: grip force
timeFrame: baseline: subject was enrolled
measure: step time
timeFrame: baseline: subject was enrolled
measure: stance time
timeFrame: baseline: subject was enrolled
measure: swing time
timeFrame: baseline: subject was enrolled
measure: step / stance length
timeFrame: baseline: subject was enrolled
measure: muscle thickness
timeFrame: baseline: subject was enrolled
measure: international Quality of Life Assessment Short Form -36 (SF-36)
timeFrame: baseline: subject was enrolled
measure: amplitude of Muscle activity
timeFrame: baseline: subject was enrolled
measure: Fear of fall scale
timeFrame: baseline: subject was enrolled
measure: Bone density
timeFrame: baseline: subject was enrolled
measure: Body composition
timeFrame: baseline: subject was enrolled
Secondary outcomes:
measure: concentration of CRP (C-Reactive Protein)
timeFrame: baseline: subject was enrolled
measure: concentration of ALB (Serum albumin)
timeFrame: baseline: subject was enrolled
measure: concentration of Glomerular Filtration Rate (GFR)
timeFrame: baseline: subject was enrolled
measure: concentration of Hemoglobin (Hb)
timeFrame: baseline: subject was enrolled
measure: concentration of Glucose SPOT
timeFrame: baseline: subject was enrolled
measure: concentration of Cholesterol
timeFrame: baseline: subject was enrolled
measure: concentration of Triglyceride
timeFrame: baseline: subject was enrolled
measure: concentration of Transferrin
timeFrame: baseline: subject was enrolled
measure: Berg balance test (BBS)
timeFrame: baseline: subject was enrolled
measure: Body Mass Index (BMI)
timeFrame: baseline: subject was enrolled
measure: Mini-mental state examination (MMSE)
timeFrame: baseline: subject was enrolled
measure: calf muscle circumference
timeFrame: baseline: subject was enrolled
measure: Timed up and go
timeFrame: baseline: subject was enrolled
Other outcomes:
Locations:
city: Changhua
country: Taiwan
facility: Changhua Christian Hospital
Study officials:
affiliation: Changhua Christian Hospital
name: TASEN WEI, MD
role: PRINCIPAL_INVESTIGATOR
References: