关键词: artificial intelligence digital therapy low back pain smartphone

来  源:   DOI:10.1177/20552076231217817   PDF(Pubmed)

Abstract:
UNASSIGNED: The present study aimed to compare the effects of a deep learning-based digital application with digital application physical therapy (DPT) and those of conventional physical therapy (CPT) on back pain intensity, limited functional ability, lower extremity weakness, radicular symptoms, limited range of motion (ROM), functional movement, quality of life, cost-effectiveness, and postintervention questionnaires for perceived transmission risk of COVID-19 and satisfaction results in 100 participants with low back pain (LBP).
UNASSIGNED: One hundred participants with LBP were randomized into either DPT or CPT groups, three times per week over four weeks. Outcome measures included the (1) Oswestry Disability Index, (2) Quebec Back Pain Disability Scale, (3) Roland-Morris Disability Questionnaire (RMDQ), (4) Numeric Pain Rating Scale, (5) functional movement screen (FMS), (6) short form-12, (7) lower extremity strength, (8) ROM of trunk flexion, extension, and bilateral side bending, (9) questionnaires for perceived transmission risk of COVID-19, (10) preliminary cost-effectiveness, and (11) postintervention satisfaction questionnaire results. The analysis of variance was conducted at p < 0.05.
UNASSIGNED: Analysis of variance showed that DPT showed superior effects, compared to CPT on RMDQ, hip extensor strength, transmission risk of COVID-19, as well as satisfaction. Both groups showed significant improvement pre- and postintervention, suggesting that DPT is as effective as CPT, and was superior in preliminary cost-effectiveness and transmission risk of COVID-19.
UNASSIGNED: Our results provide novel, promising clinical evidence that DPT was as effective as CPT in improving structural and functional impairment, activity limitation, and participation restriction. Our results highlight the successful incorporation of DPT intervention for clinical outcome measures, lower extremity strength, trunk mobility, ADL improvement, QOL improvement, and FMS in LBP.
摘要:
本研究旨在比较基于深度学习的数字应用与数字应用物理治疗(DPT)和常规物理治疗(CPT)对背痛强度的影响。有限的功能能力,下肢无力,神经根症状,有限的运动范围(ROM),功能性运动,生活质量,成本效益,和对100名腰背痛(LBP)参与者的COVID-19感知传播风险和满意度的干预后问卷结果。
100名患有LBP的参与者被随机分为DPT或CPT组,四周内每周三次。成果衡量标准包括(1)Oswestry残疾指数,(2)魁北克背痛残疾量表,(3)罗兰-莫里斯残疾问卷(RMDQ),(4)数字疼痛评定量表,(5)功能运动屏幕(FMS),(6)短形式-12,(7)下肢强度,(8)躯干屈曲ROM,扩展,和双侧侧弯曲,(9)COVID-19感知传播风险问卷,(10)初步成本效益,(11)干预后满意度问卷结果。在p<0.05时进行方差分析。
方差分析表明,DPT表现出优异的效果,与RMDQ上的CPT相比,髋伸肌力量,COVID-19的传播风险以及满意度。两组在干预前后均有明显改善,表明DPT和CPT一样有效,在COVID-19的初步成本效益和传播风险方面均较好。
我们的结果提供了新颖的,有希望的临床证据表明,DPT在改善结构和功能损害方面与CPT一样有效,活动限制,参与限制。我们的结果突出了DPT干预临床结果指标的成功结合,下肢力量,躯干移动性,ADL改进,QOL改进,和LBP的FMS。
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