Suppr超能文献

在腰痛随机对照试验中寻找稳定化训练依从性的预测因素:使用机器学习技术进行个体数据再分析

Finding Predictive Factors of Stabilization Exercise Adherence in Randomized Controlled Trials on Low Back Pain: An Individual Data Reanalysis Using Machine Learning Techniques.

作者信息

Pfeifer Ann-Christin, Schröder-Pfeifer Paul, Schiltenwolf Marcus, Vogt Lutz, Schneider Christian, Platen Petra, Beck Heidrun, Wippert Pia-Maria, Engel Tilman, Wochatz Monique, Mayer Frank, Niederer Daniel

机构信息

Pain Management, Center of Orthopaedics and Trauma Surgery, Heidelberg University Hospital, Heidelberg, Germany.

Department of Sports Medicine and Exercise Physiology, Goethe University Frankfurt, Frankfurt am Main, Germany.

出版信息

Arch Phys Med Rehabil. 2025 May;106(5):738-749. doi: 10.1016/j.apmr.2024.12.015. Epub 2025 Jan 3.

Abstract

OBJECTIVE

To identify predictors of adherence in supervised and self-administered exercise interventions for individuals with low back pain.

DESIGN

Cohort study.

SETTING

Rehabilitation.

PARTICIPANTS

This preplanned reanalysis within the Medicine in Spine Exercise Network included 1511 participants with low back pain (57% female, mean age 40.9 years, SD ±14 years).

INTERVENTIONS

Participants underwent an initial 3-week supervised phase of sensorimotor exercises, followed by a 9-week self-administered phase.

MAIN OUTCOME MEASURES

Biological, psychological, and social factors potentially impacting training adherence were evaluated. During the supervised phase, adherence was tracked through a standardized training log. During the self-administered phase, adherence was monitored via a diary, with adherence calculated as the percentage of scheduled versus completed sessions. Adherence was analyzed both as an absolute percentage and as a dichotomized variable (adherent vs nonadherent, with a 70% adherence cut-off). Predictors for adherence were identified using Gradient Boosting Machines and Random Forests (R package caret). Seventy percent of the observations were used for training, whereas 30% were retained as a hold-out test-set.

RESULTS

The average overall adherence was 64% (±31%), with 81% (±28%) adherence during the supervised phase and 58% (±39%) in the self-administered phase. The root mean square error for the test-set ranged from 36.2 (R=0.18, self-administered phase) to 19.3 (R=0.47, supervised phase); prediction accuracy for dichotomized models was between 64% and 83%. Predictors of low to intermediate adherence included poorer baseline postural control, decline in exercise levels, and fluctuations in pain intensity (both increases and decreases).

CONCLUSION

Identified predictors could aid in recognizing individuals at higher risk for nonadherence in low back pain exercise therapy settings.

摘要

目的

确定腰痛患者在监督式和自我管理式运动干预中坚持治疗的预测因素。

设计

队列研究。

地点

康复机构。

参与者

这项在脊柱运动医学网络内预先计划的重新分析纳入了1511名腰痛患者(57%为女性,平均年龄40.9岁,标准差±14岁)。

干预措施

参与者先接受为期3周的感觉运动练习监督阶段,随后是为期9周的自我管理阶段。

主要结局指标

评估可能影响训练依从性的生物学、心理学和社会因素。在监督阶段,通过标准化训练日志跟踪依从性。在自我管理阶段,通过日记监测依从性,依从性计算为计划疗程与完成疗程的百分比。依从性既作为绝对百分比进行分析,也作为二分变量(依从与不依从,依从性临界值为70%)进行分析。使用梯度提升机和随机森林(R包caret)确定依从性的预测因素。70%的观察值用于训练,而30%保留作为留出测试集。

结果

总体平均依从性为64%(±31%),监督阶段的依从性为81%(±28%),自我管理阶段为58%(±39%)。测试集的均方根误差范围为36.2(R=0.18,自我管理阶段)至19.3(R=0.47,监督阶段);二分模型的预测准确率在64%至83%之间。低至中等依从性的预测因素包括较差的基线姿势控制、运动水平下降以及疼痛强度波动(增加和减少)。

结论

确定的预测因素有助于识别腰痛运动治疗环境中不依从风险较高的个体。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验