Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty

Abstract

Background: Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics.

Methods: As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications.

Results: It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score.

Conclusion: This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

Keywords: digital health technology; frailty; gait; machine learning; prediction model; wearable sensor.

Figure 1

Overall data acquisition, feature extraction, feature selection, data classification analysis and machine learning flow for the classification of frailty.

Figure 2

Predictive performance of different feature selection in predict the outcomes of frailty model. The RF model demonstrated the most favorable performance. (A) Demographic and clinical prediction (AUC = 0.907), (B) gait sequence prediction (AUC = 0.855), (C) all feature prediction (AUC = 0.926), (D) selected feature prediction (AUC = 0.969).

Figure 3

SHAP plot of top features influencing our model’s prediction of frailty using all features.