โก Quick Summary
This study explored the use of machine learning to detect frailty in older adults residing in long-term care (LTC) facilities by analyzing data from a single accelerometer. The extreme gradient boosting model achieved an impressive accuracy of 86.3% and an AUC of 0.92, highlighting the potential for improved frailty detection methods.
๐ Key Details
- ๐ Dataset: 51 participants aged 85.0 years on average
- ๐งฉ Features used: 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables
- โ๏ธ Technology: Extreme gradient boosting model
- ๐ Performance: Accuracy 86.3%, AUC 0.92
๐ Key Takeaways
- ๐ต Frailty affects over 50% of older adults in LTC, making early detection crucial.
- ๐ Machine learning can effectively identify frailty using accelerometer data.
- ๐ Dynamic gait outcomes may be more sensitive indicators of frailty than traditional measures.
- ๐ก The study utilized a 5-meter walking task to gather gait data.
- ๐ค Explainable AI techniques were employed to enhance model interpretability.
- ๐ Findings suggest that gait patterns in frail individuals are more variable and complex.
- ๐ Study conducted as part of a larger randomized controlled trial.
๐ Background
Frailty is a common syndrome among older adults, particularly those in long-term care settings, and is associated with increased vulnerability and adverse health outcomes. Traditional methods of assessing frailty often rely on subjective measures, which can lead to inconsistencies in diagnosis and management. The integration of wearable sensors and machine learning presents a promising avenue for continuous monitoring and early detection of frailty, potentially allowing for timely interventions.
๐๏ธ Study
This study was a cross-sectional analysis of baseline data from a 2-arm cluster randomized controlled trial involving 164 individuals. Out of these, 51 participants met the inclusion criteria and completed all necessary assessments. The researchers aimed to determine whether machine learning models could accurately classify frailty status based on data collected from a 3D accelerometer during a walking task and subsequent daily activity monitoring over one week.
๐ Results
The extreme gradient boosting model emerged as the most effective, achieving an accuracy of 86.3% and an AUC of 0.92. The analysis revealed that older adults with frailty exhibited gait patterns characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score, indicating more complex and asymmetric movements compared to their non-frail counterparts.
๐ Impact and Implications
The implications of this study are significant for the field of geriatric care. By utilizing machine learning and dynamic gait analysis, healthcare providers can enhance the detection and management of frailty in LTC settings. This approach not only offers a more objective assessment but also opens the door for personalized interventions that could improve the quality of life for older adults. The potential for broader applications in healthcare is immense, paving the way for innovative monitoring solutions.
๐ฎ Conclusion
This study highlights the transformative potential of machine learning in the detection of frailty among older adults in long-term care. By focusing on dynamic gait outcomes, we can achieve more sensitive and accurate assessments, ultimately leading to better management strategies. As technology continues to evolve, the integration of AI in healthcare will likely play a crucial role in enhancing patient outcomes and care quality.
๐ฌ Your comments
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Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study.
Abstract
BACKGROUND: Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped.
OBJECTIVE: This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer.
METHODS: This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes.
RESULTS: The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score.
CONCLUSIONS: Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.
Author: [‘Zheng X’, ‘Zeng Z’, ‘S van Schooten K’, ‘Yang Y’]
Journal: JMIR Aging
Citation: Zheng X, et al. Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study. Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study. 2025; 8:e77140. doi: 10.2196/77140