The model combining wearable device and application data achieved an AUC of 0.86 for predicting (RLS) symptom groups.
Machine learning models were developed to distinguish between non-RLS and RLS symptom groups based on integrated lifestyle and biometric data.
The random forest model demonstrated the highest performance in predicting RLS symptoms.
When using only wearable device data, the model for RLS symptoms achieved an accuracy of 0.70.
Combining wearable and application data improved accuracy to 0.76 for RLS symptoms.
For severe RLS symptom prediction, the XGB model achieved an accuracy of 0.84 using only wearable device data.
Simplified
(RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance on subjective reporting. This study aimed to propose a predictive machine learning model based on digital phenotypes for RLS diagnosis. Self-reported lifestyle data were integrated via a smartphone application with objective biometric data from wearable devices to obtain 85 features processed based on circadian rhythms. Prediction models used these features to distinguish between the non-RLS (International Restless Legs Study Group Severity Rating Scale [IRLS] score ≤ 10) and RLS symptom groups (10 < IRLS ≤ 20) and between the non-RLS and severe RLS symptom groups (IRLS > 20). The RF model showed the highest performance in predicting the RLS symptom group and XGB model in the severe RLS symptom group. For the RLS symptom group, when using only wearable device data, the AUC, accuracy, precision, recall, and F1 scores were 0.78, 0.70, 0.66, 0.84, and 0.74, respectively, while these scores combining wearable device and application data were 0.86, 0.76, 0.68, 1.00, and 0.81, respectively. For the severe RLS symptom group, when using only wearable device data, XGB achieved AUC, accuracy, precision, recall, and F1 scores of 0.66, 0.84, 0.89, 0.93, and 0.91, respectively, while these scores combining wearable device and application data were 0.70, 0.80, 0.88, 0.90, and 0.89, respectively. Diverse digital phenotypes clinically associated with RLS were processed based on circadian rhythms to demonstrate the potential of for RLS prediction. Thus, our study establishes early detection and personalized management of RLS.Trial Registration: Clinical Research Information Service (CRIS) KCT0009175 (Registration data: Feb-15-2024) ( https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=26133&status=5&seq_group=26133 ).
Key numbers
0.86
AUC for symptom prediction
Achieved by the RF model with wearable and application data.
0.70
AUC for severe symptom prediction
Utilizing both wearable device and application data.
119 of 338
Participants with insomnia
Insomnia group included in the overall participant count.
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Declarations. Competing interests: The authors declare no competing interests. Human ethics and consent to participate: All study procedures were reviewed and approved by the Institutional Review Board (IRB) of Korea University Anam Hospital (IRB No. 2022AN0587). Written informed consent was obtained from all the participants.
