Healthy adults
Conditions
Interventions
Lateral and posterior lower-leg images will be captured once using a smartphone and a digital camera. On the same day, skeletal muscle mass will be measured once using bioelectrical impedance analysis
Sponsors
Kurume University
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: 1. Individuals who provide written informed consent to participate in the study 2. Healthy adults aged 18 years or older 3. Individuals capable of maintaining standing and sitting positions during assessment 4. Individuals able to undergo lower-leg image acquisition and bioelectrical impedance analysis
Exclusion criteria
Exclusion criteria: 1. Individuals who have difficulty maintaining a standing position 2. Individuals with edema or other conditions affecting the lower leg 3. Individuals with implanted medical devices such as cardiac pacemakers 4. Individuals with any limb amputation 5. Individuals with metallic implants or fixation devices in the body 6. Individuals who are pregnant or may be pregnant 7. Individuals with a history of fractures or ligament injuries of the lower limbs 8. Individuals with a history of central or peripheral nervous system disorders 9. Individuals deemed unsuitable for participation in the study by the principal investigator or co-investigators
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Concordance between skeletal muscle mass estimated from lower-leg images and skeletal muscle mass measured by bioelectrical impedance analysis (InBody 470), assessed using Lin's concordance correlation coefficient (CCC) | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. Prediction error of the skeletal muscle mass estimation model using smartphone-captured lower-leg images, assessed by mean absolute percentage error (MAPE) and root mean squared error (RMSE) 2. Agreement between skeletal muscle mass estimated from smartphone-captured lower-leg images and skeletal muscle mass measured by bioelectrical impedance analysis, assessed using Bland-Altman analysis (mean difference and 95% limits of agreement) 3. Comparison of predictive performance (CCC, MAPE, and RMSE) between the smartphone image-based skeletal muscle mass estimation model and the calf circumference-based estimation model 4. Comparison of skeletal muscle mass estimation performance according to imaging device (smartphone or digital camera) and imaging direction (lateral or posterior lower-leg view), assessed using CCC, MAPE, and RMSE | — |
Countries
Japan
Contacts
Public ContactHiroo Matsuse
Kurume University Hospital Department of Rehabilitation
Outcome results
None listed