Effectiveness of exercise programs to reduce low back pain among nurses and nursing assistants: A systematic review and meta-analysis.

INTRODUCTION: Nurses have a high prevalence of low back pain due to ergonomic hazards in healthcare workplaces. While exercise programs have been suggested as an intervention strategy, the effectiveness of low back pain programs has been inconsistent in the research literature. The purpose of study is to determine the effect of exercise programs to reduce low back pain among nursing staff. METHODS: A systematic review and meta-analysis was conducted with five databases and systematically searched. Following the PRISMA guidelines, included studies evaluated low back pain relief among nurses or nursing assistants and described the exercise program. Two reviewers independently appraised, extracted, and synthesized all available studies. The study protocol was registered in PROSPERO (CRD42022359511). RESULTS: A total of 296 articles with 1,355 nursing staff from nine countries were obtained. Nine randomized controlled trials with a moderate to low risk of bias quality were included. Exercise programs had a small but significant effect on low back pain of nursing staff (SMD = -0.48; 95% CI = -0.76 to -0.19; p = 0.03, I2 = 62%, p = 0.001). A subgroup analysis of nurses and nursing assistants showed moderate and small effects, respectively (I2 = 0% p < 0.0001, SMD -0.73 CI 95% [-0.97 to -0.48], p = 0.76, and I2 = 0% p = 0.002, SMD -0.23 CI 95% [-0.38 to -0.08], p < 0.88). Exercise for back and trunk exhibited a moderate effect on low back pain (SMD -0.56 CI 95% [-0.86 to -0.25], p = 0.01, I2 = 66%, p < 0.0004). A subgroup analysis comparing age, under 40 years old revealed a moderate effect size (SMD = -0.59; 95% CI = -0.83to -0.35; p = 0.06; I2 = 64%, p < 0.0001). CONCLUSIONS: Exercise programs are an effective treatment to reduce low back pain in nurses and nursing assistants, especially among younger staff. PRACTICAL APPLICATION: Back and trunk exercise programs should be recommended for nursing staff with low back pain.

Machine learning approach to determine the decision rules in ergonomic assessment of working posture in sewing machine operators.

INTRODUCTION: There are some inherent problems with the use of observation methods in the ergonomic assessment of working posture, namely the stability and precision of the measurements. This study aims to use a machine learning (ML) approach to avoid the subjectivity bias of observational methods in ergonomic assessments and further identify risk patterns for work-related musculoskeletal disorders (WMSDs) among sewing machine operators. METHODS: We proposed a decision tree analysis scheme for ergonomic assessment in working postures (DTAS-EAWP). First, DTAS-EAWP used computer vision-based technology to detect the body movement angles from the on-site working videos to generate a dataset of risk scores through the criteria of Rapid Entire Body Assessment (REBA) for sewing machine operators. Second, data mining techniques (WEKA) using the C4.5 algorithm were used to construct a representative decision tree (RDT) with paths of various risk levels, and attribute importance analysis was performed to determine the critical body segments for WMSDs. RESULTS: DTAS-EAWP was able to recognize 11,211 samples of continuous working postures in sewing machine operation and calculate the corresponding final REBA scores. A total of 13 decision rules were constructed in the RDT, with over 95% prediction accuracy and 83% path coverage, to depict the possible risk tendency in the working postures. Through RDT and attribute importance analysis, it was identified that the lower arm and the upper arms exhibited as critical segments that significantly increased the risk levels for WMSDs. CONCLUSIONS: This study demonstrates that ML approach with computer vision-based estimation and DT analysis are feasible for comprehensively exploring the decision rules in ergonomic assessment of working postures for risk prediction of WMSDs in sewing machine operators. PRACTICAL APPLICATIONS: This DTAS-EAWP can be applied in manufacturing industries to automatically analyze working postures and identify risk patterns of WMSDs, leading to the development of effectively preventive interventions.

Development of an educational protocol for ergonomic risk assessment of working postures to enhance the competence of occupational health nurses.

AIM: To develop a protocol and provide a valid, evidence-based procedure for identifying the ergonomic risk of working postures by occupational health nurses. BACKGROUND: Although ergonomic risk assessment tools have been used for the early detection of risky working postures, their operational procedures and validations do not target the competence of occupational nursing personnel. DESIGN: This study developed and validated an educational protocol, comprised of 13 procedures in five stages. First, the number of work tasks in the workplace is determined. Second, the working postures are confirmed. Third, the raters are trained to use the assessment tools. Fourth, high-risk postures are identified and categorized. Fifth, the inter-rater reliability of the tool is reported. The content of the protocol is validated by experts, with a validity value of 0.87. DATA SOURCES: The protocol was created through review of literature published from 1991 to 2021, protocol development (between 2018 to 2020) and expert validation (2020). CONCLUSION: The protocol can be applied to educate occupational health nurses and increase their competence in detecting workers' ergonomic risks. It can be used as a reference in occupational health nursing education to evaluate work tasks and detect risky postures.