Jeong In Cheol, Liu Jiazhen, Finkelstein Joseph
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Stud Health Technol Inform. 2019;257:189-193.
The goal of this study was to identify predictors of telerehabilitation adherence in patients with multiple sclerosis (MS). An adherence prediction model was based on baseline patient characteristics. Such a model may be useful for identifying patients who require higher levels of engagements in the early stages of home telerehabilitation programs. The resulting set of predictive features included education, patient satisfaction with the program, and psychological domain of the MS Impact Scale. Resulting prediction of high and low adherence had overall 80.0% accuracy, 81.8% sensitivity, and 77.8% specificity. We concluded that the baseline patient information may be instrumental in personalizing levels of support and training necessary for active patient participation in telerehabilitation.
本研究的目的是确定多发性硬化症(MS)患者远程康复依从性的预测因素。依从性预测模型基于患者的基线特征。这样的模型可能有助于识别在家庭远程康复计划早期阶段需要更高参与度的患者。最终得到的一组预测特征包括教育程度、患者对该计划的满意度以及MS影响量表的心理领域。由此得出的高依从性和低依从性预测的总体准确率为(80.0%),灵敏度为(81.8%),特异度为(77.8%)。我们得出结论,患者的基线信息可能有助于为患者积极参与远程康复所需的支持和培训水平进行个性化定制。