JMIR Publications

ABSTRACT

Background:

Increasing life expectancy has increased focus on health-related consequences of aging, such as sarcopenia and frailty. Given their prevalence among older individuals and frequent long-term care needs, early detection and intervention are crucial.

Objective:

To validate a novel smartphone-based system measuring acceleration during the sit-to-stand movement to detect sarcopenia and frailty.

Methods:

The study included 556 participants from the 2011 Otassha study cohort who underwent health checkups in 2023. A smartphone application, complemented by auditory prompts from a Bluetooth speaker, recorded acceleration at 60 Hz as participants performed sit-to-stand movements twice. Sarcopenia and frailty were classified using the Asian Working Group for Sarcopenia 2019 consensus and revised Japanese version of the Cardiovascular Health Study criteria, respectively. Logistic regression with stepwise variable reduction was used to develop sarcopenia and frailty predictive models, including variables of peak force (f), time from the start of movement to peak acceleration (t1), time from peak acceleration to attainment of a stable stance (t2), sex, age, and weight.

Results:

We identified 95 participants with sarcopenia (25 men and 70 women) and 51 with frailty (22 men and 29 women). Sarcopenia and frailty predictive models exhibited robust areas under receiver operating characteristic curves of 0.821 and 0.741, respectively.

Conclusions:

Smartphone accelerometers can assess lower-extremity power reliably and contribute to sarcopenia and frailty diagnoses. The predictive models, suitable for home use, could aid in early detection of and intervention for these conditions, underscoring the growing utility of smartphone-based diagnostics for addressing age-related health concerns.