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Work-related Musculoskeletal Disorders in Indian Nurses: A Cross-sectional Study

Deepti Majumdar, Pal MS and Majumdar D

It is established that nurses suffer from varying degrees of musculoskeletal disorders (MSD) in different regions of the body which results in frequent loss of work days. There are about 1.5 million registered nurses employed in health sector in India. Out of these, a majority are taking care of the health of 1.2 million strong Indian military personnel. However the prevalence of MSD in these nurses has not yet been explored till date.

A detailed study of the MSD occurring in Indian nurses (n=627) working in Indian Army was carried out using standard Nordic Musculoskeletal Questionnaire and Borg’s scale of Rating of Perceived Exertion. The participants completed the Questionnaires in presence of the authors without disclosing their identity. The data collected was tabulated and analyzed for aspects like frequencies of MSD, body parts affected, normal activities prevented, duration of pain in various regions of the body, doctor visited, duty changed, etc. One year prevalence of low back (LB) pain was found to be maximum (67.0%) followed by pain in neck (47.4%) and ankles/feet (36.0%). Musculoskeletal disorders were found to be more widespread among the nurses in middle age group (31-40 years) closely followed by youngest group (21-25 years). Among the subjects studied, married subjects were found to face more problems due to upper body MSD and single nurses suffered more from lower body MSD.

The study concluded that nurses in India were suffering from high level of MSD which could be comparable with international studies with younger age group being at higher risk. This study has highlighted the health status of care givers in Indian Armed Forces and indicates the need to develop preventive strategies to minimise the health risk of these personnel. This would enhance the performance of our nurses and improve the quality of health care services.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado.