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Background <p>Detecting new or worsening hypomobility in acute ischemic cerebrovascular patients is challenging, especially when asleep or unattended. This study used a wearable movement acceleration monitoring system to identify changes in these patients, aiming to improve early detection.</p> Methods <p>Continuous bilateral upper limb acceleration data, clinical characteristics, specific treatments, stroke etiology, and in-hospital outcomes were collected from patients. The primary outcome was newly emerging or worsening hypomobility during monitoring. An XGBoost model, trained with synthetic minority oversampling to address class imbalance and validated via 5-fold cross-validation, analyzed movement acceleration features to diagnose hypomobility timing. Model performance was evaluated through AUC and feature importance metrics.</p> Results <p>From April 2023 to February 2025, 85 patients with acute ischemic cerebrovascular events were enrolled; three were excluded due to data errors. A total of 82 patients were included in the analysis, comprising 76 (92.7%) with ischemic stroke and 6 (7.3%) with transient ischemic attack. Among them, 26 (31.7%) were women. The median monitoring duration was 26.9 hours (IQR: 24.6–46.5), with 15 patients (18.3%) developing hypomobility. The XGBoost model achieved an AUC of 0.975 (95% CI: 0.965–0.985) and a mean AUC of 0.975 (SD 0.003) across folds. Optimized with a learning rate of 0.1, maximum depth of 6, and 200 boosting rounds, the model, at a cutoff of 0.587, recorded an average sensitivity of 0.969 and specificity of 0.900, accurately detecting 96.9% of the hypomobility cases. The overall metrics included a sensitivity of 0.966, specificity of 0.900, positive predictive value of 0.896, negative predictive value of 0.968, and F1-score of 0.930. The SHAP (SHapley Additive exPlanations) analysis revealed the significant contributions of the interaction terms (mean |SHAP| = 3.475) and slope features for movement changes (e.g., 1-min RSMA and LSMA slopes), while elevating the importance of the ‘Likely weak side’ predictor (mean |SHAP| = 2.053) in orienting asymmetry.</p> Conclusion <p>This wearable movement acceleration monitoring system, by continuously tracking upper limb acceleration data, effectively detects the onset of hypomobility in acute ischemic cerebrovascular patients, highlighting its substantial potential for clinical application in enabling timely interventions and improving patient outcomes.</p>

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Performance of a wearable movement tracking system in detecting hypomobility in acute ischemic cerebrovascular events

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Duc T. Ha,
  • Van Binh Nguyen,
  • An T. T. Vo,
  • Dieu T. Truong,
  • Tuan V. Nguyen

摘要

Background

Detecting new or worsening hypomobility in acute ischemic cerebrovascular patients is challenging, especially when asleep or unattended. This study used a wearable movement acceleration monitoring system to identify changes in these patients, aiming to improve early detection.

Methods

Continuous bilateral upper limb acceleration data, clinical characteristics, specific treatments, stroke etiology, and in-hospital outcomes were collected from patients. The primary outcome was newly emerging or worsening hypomobility during monitoring. An XGBoost model, trained with synthetic minority oversampling to address class imbalance and validated via 5-fold cross-validation, analyzed movement acceleration features to diagnose hypomobility timing. Model performance was evaluated through AUC and feature importance metrics.

Results

From April 2023 to February 2025, 85 patients with acute ischemic cerebrovascular events were enrolled; three were excluded due to data errors. A total of 82 patients were included in the analysis, comprising 76 (92.7%) with ischemic stroke and 6 (7.3%) with transient ischemic attack. Among them, 26 (31.7%) were women. The median monitoring duration was 26.9 hours (IQR: 24.6–46.5), with 15 patients (18.3%) developing hypomobility. The XGBoost model achieved an AUC of 0.975 (95% CI: 0.965–0.985) and a mean AUC of 0.975 (SD 0.003) across folds. Optimized with a learning rate of 0.1, maximum depth of 6, and 200 boosting rounds, the model, at a cutoff of 0.587, recorded an average sensitivity of 0.969 and specificity of 0.900, accurately detecting 96.9% of the hypomobility cases. The overall metrics included a sensitivity of 0.966, specificity of 0.900, positive predictive value of 0.896, negative predictive value of 0.968, and F1-score of 0.930. The SHAP (SHapley Additive exPlanations) analysis revealed the significant contributions of the interaction terms (mean |SHAP| = 3.475) and slope features for movement changes (e.g., 1-min RSMA and LSMA slopes), while elevating the importance of the ‘Likely weak side’ predictor (mean |SHAP| = 2.053) in orienting asymmetry.

Conclusion

This wearable movement acceleration monitoring system, by continuously tracking upper limb acceleration data, effectively detects the onset of hypomobility in acute ischemic cerebrovascular patients, highlighting its substantial potential for clinical application in enabling timely interventions and improving patient outcomes.