Abstract Background Increasing life expectancy has increased focus on the health-related consequences of aging, such as sarcopenia and frailty. Given the prevalence of these conditions among older individuals and the frequent resulting long-term care needs, early detection and intervention are crucial. Objective This study aimed to validate a novel smartphone-based system measuring acceleration during the sit-to-stand movement to detect sarcopenia and frailty. Methods Participants were 587 individuals from the Otassha study cohort who underwent health assessments in 2023, of whom 569 (96.9%) completed 2 supervised sit-to-stand trials while holding a smartphone on the lower abdomen. Sarcopenia and frailty were diagnosed using the Asian Working Group for Sarcopenia 2019 criteria and the revised Japanese Cardiovascular Health Study criteria, respectively. Peak force, rising time (T1), and stabilization time (T2) were extracted from acceleration signals, and reproducibility was examined using the intraclass correlation coefficient (ICC(2,1)). Predictive models were developed using elastic net penalized logistic regression, and model performance was evaluated using 500 bootstrap resamples. Benchmark models using age and sex, walking speed, and grip strength were also constructed for comparison. Results Sarcopenia and frailty were identified in 16.7% (95/569) and 9% (51/569) of the participants, respectively. Peak force demonstrated excellent reliability (ICC=0.863), whereas T1 and T2 showed lower reproducibility (ICC<0.30). For sarcopenia, the smartphone model achieved a bootstrap area under the receiver operating characteristic curve (AUC) of 0.800 and an optimism-corrected AUC of 0.781 (95% CI 0.733‐0.826), outperforming walking speed (0.663) and age and sex (0.656) and ranking second only to grip strength (0.845). For frailty, the smartphone model showed moderate discrimination, with an optimism-corrected AUC of 0.659 (95% CI 0.587‐0.736), exceeding age and sex (0.604), whereas walking speed remained the strongest predictor (0.751). Conclusions Smartphone-derived sit-to-stand acceleration provides a practical and scalable approach for screening for sarcopenia and frailty in community-dwelling older adults. While traditional indicators such as grip strength and walking speed remain the most accurate predictors, smartphone-based measurements offer meaningful complementary information and may support large-scale functional screening and early detection initiatives in superaged societies.
Obuchi et al. (Mon,) studied this question.