Association of Physical Activity and Quality of Life with Work-Related Musculoskeletal Disorders in the UAE Young Adults

Concurrent validity between self-reported International Physical Activity Questionnaire Short Form and Fibion accelerometer data among young adults in the UAE

Self-reported physical activity questionnaires (e.g., International Physical Activity Questionnaire, IPAQ) are a cost-effective, time-saving, and accessible method to assess sedentary behaviour and physical activity. There are conflicting findings regarding the validity of self-reported questionnaires in comparison to accelerometer-measured data in a free-living environment. This study aimed to investigate the concurrent validity between self-reported Arabic-English IPAQ short form (IPAQ-SF) and Fibion (Fibion Inc., Jyväskylä, Finland) accelerometer-measured sedentary and physical activity time among young adults. One hundred and one young healthy adults (mean age 20.8 ± 2.4 years) filled in the IPAQ short form (IPAQ-SF) and wore the Fibion device on the anterior thigh for ≥ 600 min per day for 4-7 days. Concurrent validity between the IPAQ-SF and Fibion accelerometer for sitting, walking, moderate activity, and vigorous activity time was assessed using the Spearman correlation coefficient ( ρ ) and Bland-Altman plots. Significant weak associations between IPAQ-SF and Fibion measurements were found for total activity time ( ρ = 0.4; P < 0.001) and for the duration of walking ( ρ = 0.3; P = 0.01), moderate ( ρ = 0.2; P = 0.02), and vigorous-intensity activities ( ρ = 0.4; P < 0.001). However, ρ was not significant ( ρ = - 0.2; P = 0.09) for sitting time. In addition, all the plots of the measured variables showed a proportional bias. A low association and agreement were found between self-reported IPAQ-SF scores and Fibion accelerometer measurements among young adults in the UAE. Adult sedentary and physical activity measurements should be obtained objectively with accelerometers rather than being limited to self-reported measures.

Mendelian randomization reveals the causal association between gout and hearing impairment in older adults

With the global aging trend escalating, the holistic well-being of the elderly has become a paramount concern within public health. Traditional observational studies often struggle with confounding factors and establishing causality, leaving the relationship between age-related hearing loss (ARHL) and gout largely unexplored. Employing bidirectional two-sample Mendelian randomization (MR) analysis, this investigation elucidated the genetic underpinnings associated with age-related hearing impairment, gout, and urate levels within the IEU Open-GWAS database, thereby uncovering potential causal connections that underlie the intricate interplay between gout, serum urate concentrations, and auditory decline in the geriatric demographic. In the forward MR phase, a cohort of 30 single nucleotide polymorphisms was leveraged to dissect the causal dynamics between ARHL and both gout and urate concentrations. Conversely, in the reverse MR phase, gout and urate levels were posited as the exposome to delineate their impact on hearing acuity, employing an array of models for rigorous validation and sensitivity scrutiny. In the forward MR analysis, a statistically significant correlation was discerned between ARHL and gout (P = .003, odds ratio = 1.01, 95% confidence interval: 1.00-1.02), alongside a notable association with serum urate levels (P = .031, odds ratio = 1.39, 95% confidence interval: 1.03-1.88), intimating that ARHL could potentially influence the incidence of gout and urate concentrations. Conversely, the reverse MR investigation revealed that neither gout nor serum urate levels exerted significant impact on auditory degradation (P > .05), insinuating that these factors might not predominantly contribute to hearing loss. Sensitivity analyses concurred with this inference. This study enriches the comprehension of geriatric health intricacies and unveils that ARHL potentially influences gout and serum urate concentrations. This suggests that monitoring ARHL may play a crucial role in the early identification and management of gout and hyperuricemia, potentially contributing to a comprehensive approach to improving geriatric health outcomes.

Work-related musculoskeletal disorders among United Arab Emirates schoolteachers: an examination of physical activity

OBJECTIVE

To estimate the prevalence of work-related musculoskeletal disorders and their association with physical activity among schoolteachers in the United Arab Emirates.

METHODS

This observational cross-sectional study involved 209 schoolteachers (aged 20-60) with a minimum of two years of experience. Data, including demographics (gender, age), Nordic Musculoskeletal Questionnaire (NMQ) for WMSD prevalence and pain sites, and Global Physical Activity Questionnaire (GPAQ) for physical activity levels, were collected. Six schools were visited for data collection, with consent from school heads and participants. Schedules were tailored to participant availability, allowing up to three attempts for participation. Non-respondents were identified after three unsuccessful attempts.

RESULTS

Of the total 206 participants, 149 were female, while the remaining 57 were male. Age distribution analysis revealed that 18% of individuals were within the 20 to 30 years range, 26.2% fell within the 30 to 40 years, and 36.9% had 40 to 50 years age brackets. The responses were obtained in Arabic (90%) and English (10%). The study identified a high prevalence (71.4%) of work-related musculoskeletal disorders (WMSD) in UAE schoolteachers, with neck pain being the most common (74.3%). Major risk factors included age, workload, and low physical activity. The data was normally distributed, and Pearson's Correlation test revealed weak positive correlation (r: 0.14), but statistically significant (p value = 0.04) between WMSD and PA, indicating that it was a contributing factor but other factors beyond PA influenced WMSD prevalence in this cohort. The findings of the study are based on certain limitations such as cross-sectional design and convenient sampling which could have potential selection bias and affect generalizability of the results.

CONCLUSION

Findings suggest the need to promote physical activity and reduce workload for teachers, considering their age and gender. Additionally, there is a need to raise awareness regarding ergonomics and the importance of taking short breaks for stretching or physical movement to enhance the overall well-being of schoolteachers in the UAE and similar contexts. Diverse prevalence rates across different body areas underscore the necessity for individualized treatments.

Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research.