BACKGROUND

Frailty syndrome in older adults represents a significant public health concern, characterized by a reduction in physiological reserves and an increased susceptibility to stressors. This can result in adverse health outcomes, including falls, hospitalization, disability, and mortality. The early identification and management of frailty are essential for improving quality of life and reducing health care costs. Conventional assessment techniques, including dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and muscle ultrasound (US), are efficacious but frequently constrained in primary care settings by financial and accessibility limitations.

OBJECTIVE

The aim of this study is to analyze the differences in anthropometric characteristics, physical function, nutritional status, cognitive status, and body composition among older adults identified as frail, prefrail, or robust in primary care services using the PowerFrail mobile app. Furthermore, the study assesses the predictive capacity of body composition variables (whole-body phase angle [WBPhA] via BIA, US-measured rectus femoris muscle thickness, and DXA-derived lean mass) in identifying frailty and evaluates their feasibility for implementation in primary care.

METHODS

A descriptive cross-sectional study was conducted with 94 older adult participants aged between 70 and 80 years, recruited through the Andalusian Health Service in Spain. Frailty status was classified using the PowerFrail App, which integrates muscle power assessment and provides personalized physical activity recommendations. Body composition was measured using WBPhA (BIA), muscle US, and DXA. Statistical analyses included 1-way ANOVA for group comparisons, logistic regression to investigate associations, and receiver operating characteristic curve analysis to evaluate the predictive accuracy of the body composition measures.

RESULTS

Participants were categorized into frail (n=28), prefrail (n=33), and robust (n=33) groups. All body composition measures exhibited high specificity in detecting frailty, with varying sensitivity. Unadjusted US showed the highest specificity but low sensitivity (10.7%). WBPhA and right leg lean mass (LeanM RL) demonstrated significant predictive capabilities, especially when adjusted for age and sex, with area under the curve values ranging from 0.678 to 0.762. The adjusted LeanM RL model showed a good balance between sensitivity (35.7%) and specificity (93.9%; P=.045), indicating its potential as a reliable frailty predictor. These findings are consistent with previous research emphasizing the importance of muscle mass and cellular health in frailty assessment.

CONCLUSIONS

Body composition variables, particularly WBPhA, LeanM RL, and US, are effective predictors of frailty in older adults. The PowerFrail mobile app, combined with advanced body composition analysis, offers a practical and noninvasive method for early frailty detection in primary care settings. Integrating such technological tools can enhance the early identification and management of frailty, thereby improving health outcomes in the aging population.