Development of the Prevent for Work questionnaire (P4Wq) for assessment of musculoskeletal risk in the workplace: part 1-literature review and domains selection

Pain interference in young adulthood and work participation

ABSTRACT

Chronic pain is associated with reduced work participation, but longitudinal data on the work impact of chronic pain are limited. We used data from the National Longitudinal Survey of Youth-1997 cohort to analyze how pain interference in early adulthood was associated with subsequent exit from the labor force in a longitudinal survey. Pain interference at age 29 and employment status were self-reported at subsequent biennial interviews. Exit from the labor force, return to employment, and development of new health-related work limitations after age 29 were analyzed using survival analysis methods. Among 5819 respondents, 10% and 3% endorsed "a little" or "a lot" of pain interference at age 29, respectively. During follow-up (median of 26 months until censoring or labor force exit), 43% of respondents had exited the labor force at least once and 10% developed a new work-related health limitation. The highest pain interference group (compared with no pain interference) had higher hazard of labor force exit (hazard ratio: 1.26; 95% confidence interval: 1.01-1.57; P = 0.044) and of developing new health-related work limitations (hazard ratio: 2.45; 95% confidence interval: 1.64-3.67; P < 0.001), with similar results for the group experiencing "a little" pain interference at age 29. In this nationally representative cohort, any level of pain interference reported at age 29 was found to predict increased hazards of subsequent labor force exit and health-related work limitation. Early identification and treatment of pain problems among young workers can help reduce burdens of future unemployment and disability.

Low Back Pain and Upper-Extremity Musculoskeletal Disorders in French Postal Workers Driving Light-Duty Vehicles for Mail and Parcel Delivery

Occupational driving of light-duty vehicles (LDVs) became increasingly important in parcel delivery faced with the explosive growth of e-commerce. Since musculoskeletal disorders (MSDs) represent the most reported driving-related health problem, we aimed to analyze the risk of low back pain (LBP) and upper-extremity musculoskeletal disorders (UEMSDs) associated with driving LDVs for parcel delivery. In 306 postal workers exposed to driving and 100 unexposed workers, information on occupational driving, physical/psychosocial constraints, and work organization were collected via a questionnaire. MSDs were assessed using the Nordic Questionnaire, 14 additional questions regarding LBP, and a standardized clinical examination for UEMSDs. Statistical modeling consisted of multivariable logistic regression for UEMSDs and the item response theory approach for LBP. UEMSDs were associated with the distance of rural rounds and inversely associated with urban/mixed delivery rounds. Handling heavy loads was associated with LBP, and high physical demands during delivery rounds were related to MSDs. Karasek dimensions and mobbing actions were associated with MSDs. Work recognition, driving training, using an automatic gearbox, and the utilization of additional staff during peak periods were inversely associated with MSDs. Our results suggest that the distance driven in rural settings and high physical demands were associated with MSDs, while some organizational factors could protect from MSDs.

The English Teaching Methods in the Field of Public Health in Colleges and Universities Based on Artificial Intelligence Technology

Artificial intelligence technology has become an important part of the development of Internet technology. Artificial intelligence technology can help colleges and universities to continuously optimize the English teaching system. This technology can help colleges and universities to carry out English education in the field of public health and can improve the overall quality of English teaching in colleges and universities. Artificial intelligence technology is related to the optimization of English teaching environment in colleges and universities. At the same time, artificial intelligence technology also affects the development of society and the future of the country. Artificial intelligence technology provides more accurate data resources for English teaching in the field of public health in colleges and universities. It also provides rich and reliable educational technology means for teachers. This technology improves the scientific nature of English education in the field of public health in colleges and universities. This paper comprehensively uses a variety of methods such as case empirical analysis and qualitative analysis to analyze the application mode of artificial intelligence technology in English teaching. This paper closely integrates artificial intelligence technology with English education in the field of public health in colleges and universities. College English teaching methods, teachers' personal factors, and teacher-student relationship will all have an impact on students' health. This paper makes a comprehensive analysis of the theoretical basis and actual situation of English teaching in colleges and universities, and then constructs an innovative system of English education in the field of public health in colleges and universities. Based on this, the text adopts a structured analysis method to conduct an in-depth analysis of the application mode of artificial intelligence technology. This paper analyzes in detail the opportunities and challenges faced by the development of public health education in colleges and universities. At the same time, this paper also summarizes the objective laws of the development of public health education, and then comprehensively analyzes the impact of artificial intelligence technology on the English education model in colleges and universities.

Development of the Prevent for Work Questionnaire (P4Wq) for the assessment of musculoskeletal risk factors in the workplace: part 2-pilot study for questionnaire development and validation

OBJECTIVE

The aim of this study was to develop a multifactorial, self-report questionnaire: Prevent for Work Questionnaire (P4Wq). The questionnaire is intended for screening for risk factors in work-related musculoskeletal disorders (WMSDs).

DESIGN

Data were collected from otherwise healthy workers employed in three service areas at a specialist hospital in Italy: healthcare, administration and ancillary services.

SETTING AND PARTICIPANTS

In all, 115 participants were enrolled (67% women; average age 41.5±9.94 years). The content of the tool for WMSDs was derived from three participation rounds of analysis involving a select group of experts who identified the questionnaire domains and items. Participants responded to 89 items in addition to the EuroQol 5 Dimensions Questionnaire (EQ-5D-5L), Fear-Avoidance Beliefs Questionnaire (FABq) and Oswestry Disability Index (ODI). The proportion of missing data and the distribution of responses were analysed for each item. Items with a discrimination index >0.40 and an interitem correlation <0.80 were retained. Factor analysis was performed using the VARIMAX rotation method, factor extraction, and identification, assignment of items to subscales, and assignment of scores to items. Internal consistency, reliability, construct validity and face validity were also assessed.

RESULTS

A total of 52 items were included in the factor analysis and four subscales identified: Physical Stress Subscore (six items); Mental Stress Subscore (six items); Job Satisfaction Subscore (four items) and Kinesiophobia/Catastrophizing Subscore (four items). The items in the final questionnaire version had a factor loading >0.7. The questionnaire consisted of 20 items with good internal consistency (Cronbach's alpha 0.81-0.91), reliability (weighted kappa coefficient 0.617-1.00), good construct validity (EQ-5D-5L, r=-0.549, p<0.001; ODI, r=0.549, p<0.001; FABq work, r=0.688, p<0.001) and satisfactory face validity (universal validity index 96.04%).

CONCLUSION

The P4Wq is a 20-item, multifactorial self-report risk assessment questionnaire. It may provide a useful tool for screening for WMSDs by specifically addressing back disorders. It investigates risks for individual workers and may inform educational programmes and preventive strategies tailored to a worker's needs.

TRIAL REGISTRATION NUMBER

NCT04192604.

European knowledge alliance for innovative measures in prevention of work-related musculoskeletal pain disorders (Prevent4Work Project): protocol for an international mixed-methods longitudinal study

INTRODUCTION

Work-related musculoskeletal (MSK) pain is a highly prevalent condition and one of the main contributors to disability and loss of work capacity. Current approaches to the management and prevention of work-related MSK pain do not consistently integrate current evidence-based knowledge and seem to be outdated. The Prevent4Work (P4W) Project aims to collect and spread evidence-based information to improve the management and prevention of work-related MSK pain. P4W will longitudinally investigate (1) risk factors associated with the prevalence of work-related MSK pain, (2) predictive factors for new events of work-related MSK pain in the short term and (3) the modification of pain beliefs after participating in evidence-based e-learning courses.

METHODS AND ANALYSIS

This project employs a mixed-methods design with international cohorts of workers from Spain, Italy and Denmark. All participants will be assessed using self-reported variables at baseline (ie, cross-sectional design) with follow-up after 3 and 6 months (ie, prospective-predictive design). Throughout the first phase (0-3 months), all participants will be offered to self-enrol in e-learning courses on work-related MSK pain. Changes in pain beliefs (if any) will be assessed. The dataset will include sociodemographic characteristics, physical and psychological job demands, lifestyle-related factors, MSK pain history and pain beliefs. At baseline, all participants will additionally complete the P4W questionnaire developed to detect populations at high risk of suffering work-related MSK pain.Descriptive statistics, binary logistic regression, and analysis of variance will be used to identify the significant factors that influence the history of work-related MSK pain, evaluate the short-term prediction capacity of the P4W questionnaire, and investigate whether workers' participation in e-learning courses will modify their pain beliefs.

ETHICS AND DISSEMINATION

The study received ethical approval from the Ethical Committee of San Jorge University (USJ011-19/20). The results will be made available via peer-reviewed publications, international conferences and P4W official channels.