JMIR Publications

ABSTRACT

Background:

Probable sarcopenia is determined by a reduction in muscle strength assessed with handgrip strength test or 5-times sit-to-stand and it is confirmed with a reduction in muscle quantity determined by dual-energy X-ray absorptiometry or bioelectrical impedance analysis. However, these parameters are not implemented in clinical practice mainly due to lack of equipment and time constraints. Nowadays, thanks to technical innovations incorporated in most smartphone devices such as high-speed video cameras provide the opportunity to develop specific smartphone applications for measuring kinematic parameters related with sarcopenia during a simple sit-to-stand transition.

Objective:

To create and validate a sit-to-stand video analysis based-App for diagnosing sarcopenia in community-dwelling older adults and to analyze its construct validity with health-related risk factors and frailty.

Methods:

A total of 686 community-dwelling older adults (median-age: 72, 59% female) were recruited from elderly social centers. The index test was a sit-to-stand video analysis based-App using muscle power and calf-circumference as a proxy of muscle strength and muscle quantity, respectively. The reference standard was obtained by different combinations of muscle strength (handgrip strength or 5-times sit-to-stand) and muscle quantity (appendicular skeletal mass or skeletal muscle index) as recommended by the EWGSOP2. Sensibility, specificity, positive and negative predictive values were calculated as well as the area under the curve (AUC) of the receiver operating characteristic to determine the diagnostic accuracy of the App. Construct validity was evaluated using logistic regressions to identify risks associated to health-related outcomes and frailty (Fried phenotype) for those individuals classified as sarcopenic by the index test.

Results:

Sarcopenia prevalence varied from 2% to 11% according to the different combinations proposed by the EWGSOP2. Sensitivity, specificity, and AUC ranged between 70–83.3%, 77–94.9%, 80.5–87.1%, respectively, depending on the diagnostic criteria used. Likewise, positive and negative predictive values ranged between 10.6–43.6% and 92.2¬–99.4%, respectively. These results proved that the App was quite reliable to rule out the disease. Moreover, those individuals diagnosed with sarcopenia according to the index test showed more odds to have health-related adverse outcomes and frailty compared to their respective counterpart regardless the definition proposed by the EWGSOP2.

Conclusions:

The App showed a good diagnostic performance for detecting sarcopenia in well-functioning Spanish community-dwelling older adults. Sarcopenic individuals diagnosed by the App showed more odds to have health-related risk factors and frailty compared to their respective counterpart. These results highlight the potential use of this App in clinical settings. Clinical Trial: ClinicalTrials.gov NCT05148351.