Evidence map›Paper›PMID 41361259›Full record

ArticleBMC geriatrics2025

Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.

Fei Liu, Haiping Luo, Yuting Huang, Yu Sun, Xia Yang, Xiaoping Zhu

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Fei LiuNursing Department, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Haiping LuoNursing Department, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yuting HuangNursing Department, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yu SunSchool of Medicine, Tongji University, Shanghai, China.
Xia YangSchool of Medicine, Tongji University, Shanghai, China.
Xiaoping ZhuNursing Department, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China. xiaopingzhu0424@163.com.

Funding

National Natural Science Foundation of China 72304210Shanghai Shenkang Hospital Development Center, the second round of "Promoting Municipal Hospital Clinical skills and Innovation three-year action Plan" research doctors innovation transformation ability training project SHDC2023CRS024
6 · The paper itself

Abstract

backgroundLow back pain imposes a substantial burden on global healthcare systems. Kinesiophobia is highly prevalent among older adults with chronic low back pain, severely hindering effective intervention and treatment. However, current assessment of kinesiophobia in this population remain inadequate, necessitating further research. This study aimed to develop a predictive model to identify key factors influencing kinesiophobia in older adults with chronic low back pain, thereby assisting clinicians in assessment and interventions.

methodsA cross-sectional study was conducted among 386 older adults with chronic low back pain admitted to the Department of Spinal Surgery and Orthopedic Rehabilitation Center between January 2024 and December 2024. The dataset was randomly split into training (70%) and testing (30%) sets. Six machine learning models, including Logistic Regression(LR), Decision Tree(DT), Random Forest(RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Artificial Neural Network(ANN), were developed and evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), calibration curves, and other metrics. Clinical utility was examined via decision curve analysis, and predictor significance was interpreted using Shapley additive explanations (SHAP).

resultsThe prevalence of kinesiophobia in the cohort was 65.3%. LightGBM and RF demonstrated robust performance across both training and testing sets. In the testing set, LightGBM achieved superior accuracy, precision, sensitivity, specificity, and F1-score. Key predictors included Oswestry Disability Index (ODI), moderate-to-severe anxiety, pain intensity, moderate-to-severe depression, frailty, smoking, and the history of falls.

conclusionThis study developed a machine learning-based predictive model for assessing kinesiophobia in older adults with chronic low back pain, which providing a reference for kinesiophobia assessment in this patients group and also identifying key intervention priorities for healthcare systems in kinesiophobia management.

Indexed as

Chronic PainLow Back PainPhobic DisordersAgedAged, 80 and overCross-Sectional StudiesFemaleHumansKinesiophobiaMachine LearningMaleMiddle AgedKinesiophobiaMachine learningPrediction model, Low back pain

Identifiers

PMID41361259
PMCPMC12683872

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.