musculoskeletal symptoms

Purpose: This study aims to develop and evaluate an adaptive ergonomic framework for reducing Musculoskeletal Disorder (MSD) risks among pre-elderly workers in Micro, Small, and Medium Enterprises (MSMEs). Methodology: This study adopted a sequential quantitative design consisting of two consecutive phases. The first phase involved a cross-sectional ergonomic assessment of 50 pre-elderly MSMEs workers using the Nordic Body Map (NBM), Rapid Entire Body Assessment (REBA), Rapid Upper Limb Assessment (RULA), and Quick Exposure Checklist (QEC). Based on the integrated assessment results, an adaptive ergonomic framework incorporating administrative controls, engineering controls, ergonomic personal protective equipment (PPE), and anthropometry-based workstation redesign was developed. The second phase evaluated the framework through a 14-day before–after intervention study in five selected MSMEs. Results: Most workers were exposed to moderate-to-very high ergonomic risks, with musculoskeletal complaints predominantly affecting the lower back, back, wrists, neck, and lower extremities. Following implementation, QEC exposure scores decreased across the five MSMEs from 68.7–84.5% before intervention to 46.3–63.2% after intervention, accompanied by reductions in REBA/RULA risk classifications and lower NBM complaint levels. Conclusion: The proposed framework effectively reduced ergonomic risks and musculoskeletal complaints among pre-elderly MSMEs workers through practical and low-cost interventions. Originality: This study contributes a comprehensive and context-specific adaptive ergonomic framework that integrates risk assessment, workstation redesign, administrative controls, and ergonomic PPE recommendations tailored to the characteristics of pre-elderly workers in resource-constrained MSMEs.

Objective This study aimed to estimate the prevalence of musculoskeletal symptoms and work-related musculoskeletal disorders (WMSDs) among orthopedic nurses, identify demographic, occupational, and lifestyle characteristics associated with WMSDs, and explore whether routinely collected characteristics could discriminate nurses with WMSDs from those without. Method A single-center cross-sectional study was conducted among nurses at a tertiary orthopedic specialty hospital in China between March and November 2023. Conventional analyses included multivariable logistic regression, modified Poisson regression with robust standard errors, and negative binomial regression. Logistic regression as a benchmark, elastic-net logistic regression, random forest, XGBoost, and support vector machine were compared using repeated 5-fold cross-validation with fold-specific preprocessing. Prediction performance was assessed using AUC with bootstrap 95% CIs, PR-AUC, Brier score, calibration intercept and slope, and descriptive threshold metrics; TreeSHAP was used for model interpretation. Result Among 948 nurses with valid questionnaires, 654 reported musculoskeletal symptoms in the previous 12 months, irrespective of work attribution (68.99%). After excluding 44 participants with uncertain work-related attribution, 604/904 participants were classified as having work-related WMSDs (66.81%). Each 1-point increase in the strict ergonomic risk score was associated with WMSDs in logistic regression (adjusted OR = 2.631, 95% CI 2.039–3.432; p < 0.001) and modified Poisson regression (adjusted PR = 1.283, 95% CI 1.203–1.369; p < 0.001). The highest observed AUC was 0.752 (95% CI 0.721–0.791) for the domain random forest model; the domain XGBoost model had the highest PR-AUC (0.841). Conclusion Musculoskeletal symptoms and WMSDs were common among orthopedic nurses in this single-center sample. Occupational exposure and postural load were associated with WMSDs. The exploratory prediction models showed limited-to-moderate discrimination and warrant prospective multicenter external validation before practical application.

Digital media has evolved from passive screen viewing into an algorithmically curated ecosystem of social platforms, short form video, games, generative artificial intelligence (AI), conversational agents, connected toys and educational technologies. These systems can support learning, creativity, communication, disability access and healthcare; however, engagement optimized design, commercial surveillance and synthetic content create risks that are not adequately captured by screen-time measures alone. This narrative review examines the effects of contemporary digital ecosystems on child and adolescent health and outlines implications for pediatric practice. PubMed/MEDLINE, Google Scholar and guidance from the World Health Organization, United Nations Children's Fund, UNESCO and the American Academy of Pediatrics were reviewed, with emphasis on evidence published from 2019 to June 2026. Associations have been reported between problematic digital media use and sleep disruption, reduced physical activity, visual and musculoskeletal symptoms, obesity related behaviours, anxiety, depressive symptoms, cyberbullying, disordered eating and impaired attention. Causal interpretation remains difficult because effects are heterogeneous, often small, bidirectional and strongly modified by age, content, context, displacement, family relationships and pre-existing vulnerability. AI adds distinct concerns: persuasive personalization, inaccurate health information, anthropomorphic dependency, deepfakes, sexual exploitation, biased outputs, privacy loss and erosion of learning through uncritical outsourcing. Benefits are also substantial when technologies are developmentally appropriate, evidence-based, inclusive and used with adult support. Pediatric counselling should shift from counting hours alone toward assessing the child's digital ecosystem, sleep, function, content, compulsivity, safety and relationships. Effective protection requires shared family practices, school digital literacy, safety by design, age-appropriate assurance, transparent algorithms, data minimization and independent research. The goal is not a screen free childhood but a child centred digital environment that supports health, agency and development.

Introduction: Polysomnography technologists perform overnight sleep studies requiring continuous patient monitoring, technical management of diagnostic equipment, and sustained attention.Although night-shift work has been associated with adverse health outcomes among healthcare workers, the occupational health burden among polysomnography technologists remains poorly characterized in India.This study evaluated sleep quality, excessive daytime sleepiness, burnout-related symptoms, musculoskeletal symptoms, and selected cardiometabolic indicators and examined their associations with years of night-shift work experience.Methods: This cross-sectional observational study used an online structured questionnaire distributed through professional groups to polysomnography technologists working in private hospitals and sleep centers in South India.Participants were graduates with at least six months of night-shift experience.Of 196 responses, 16 were excluded for less than six months of night-shift experience and 30 for incomplete questionnaires, leaving 150 participants for analysis.The questionnaire included the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Copenhagen Burnout Inventory (CBI), and musculoskeletal assessment.Height and weight were measured by participants.Blood pressure was manually assessed, while fasting blood glucose and glycated hemoglobin (HbA1c) were obtained from recent laboratory reports.Poor sleep quality was defined as PSQI >5 and excessive daytime sleepiness as ESS >10.The CBI was analyzed continuously and categorized operationally as 0-49, 50-74, and 75-100.Years of night-shift work experience were analyzed as a continuous exposure and categorized as ≤5 versus >5 years.Multivariable logistic regression was adjusted for age, sex, and BMI.Results: Mean age was 29.97 ± 4.43 years, and BMI was 24.16 ± 3.19 kg/m².Mean PSQI, ESS, and CBI scores were 7.09 ± 2.95, 9.20 ± 3.39, and 45.99 ± 20.95, respectively.Poor sleep quality occurred in 97 (64.7%), excessive daytime sleepiness in 56 (37.3%), and musculoskeletal symptoms in 126 (84.0%).CBI scores were categorized as 0-49 in 86 (57.3%), 50-74 in 48 (32.0%), and 75-100 in 16 (10.7%).Hypertension-range blood pressure was identified in 21 (14.0%) and prediabetes in 11 (7.3%).Each additional year of night-shift work experience was associated with higher odds of poor sleep quality (adjusted odds ratio (aOR): 1.433, 95% CI: 1.118-1.835),excessive daytime sleepiness (aOR: 1.205, 95% CI: 1.001-1.450),CBI ≥50 (aOR: 1.810, 95% CI: 1.350-2.428),musculoskeletal symptoms (aOR: 1.372, 95% CI: 1.003-1.878),lower-back symptoms (aOR: 1.296, 95% CI: 1.051-1.598),neck symptoms (aOR: 1.272, 95% CI: 1.043-1.552),and hypertension-range blood pressure (aOR: 1.387, 95% CI: 1.052-1.829).No significant associations were observed with shoulder symptoms or prediabetes.Conclusion: Polysomnography technologists reported frequent poor sleep quality, excessive daytime sleepiness, burnout-related symptoms, and musculoskeletal symptoms.More years of night-shift work experience were associated with several adverse health outcomes after adjustment for age, sex, and BMI.These findings should be interpreted cautiously given the cross-sectional design and potential residual confounding.Prospective multicenter studies are warranted.

مقدمه: تکنولوژیست‌های پلی‌سومنوگرافی مطالعات خواب شبانه را انجام می‌دهند که نیازمند پایش مداوم بیماران، مدیریت فنی تجهیزات تشخیصی و توجه پایدار است. اگرچه کار شیفت شب با پیامدهای نامطلوب سلامت در میان کارکنان مراقبت‌های بهداشتی مرتبط دانسته شده است، بار سلامت شغلی در میان تکنولوژیست‌های پلی‌سومنوگرافی در هند به‌خوبی توصیف نشده است. این مطالعه کیفیت خواب، خواب‌آلودگی بیش از حد در طول روز، علائم مرتبط با فرسودگی شغلی، علائم اسکلتی‌عضلانی و برخی شاخص‌های قلبی‌متابولیک را ارزیابی کرد و ارتباط آن‌ها را با سال‌های تجربه کار شیفت شب بررسی نمود. روش‌ها: این مطالعه مشاهده‌ای مقطعی با استفاده از پرسشنامه ساختاریافته آنلاین که از طریق گروه‌های حرفه‌ای توزیع شد، در میان تکنولوژیست‌های پلی‌سومنوگرافی شاغل در بیمارستان‌ها و مراکز خواب خصوصی در جنوب هند انجام شد. شرکت‌کنندگان فارغ‌التحصیلانی با حداقل شش ماه تجربه کار شیفت شب بودند. از مجموع ۱۹۶ پاسخ، ۱۶ نفر به دلیل کمتر از شش ماه تجربه کار شیفت شب و ۳۰ نفر به دلیل پرسشنامه ناقص حذف شدند و ۱۵۰ نفر برای تحلیل باقی ماندند. پرسشنامه شامل شاخص کیفیت خواب پیتسبورگ (PSQI)، مقیاس خواب‌آلودگی اپورث (ESS)، فهرست فرسودگی شغلی کپنهاگ (CBI) و ارزیابی اسکلتی‌عضلانی بود. قد و وزن توسط شرکت‌کنندگان اندازه‌گیری شد. فشار خون به صورت دستی ارزیابی شد و قند خون ناشتا و هموگلوبین گلیکوزیله (HbA1c) از گزارش‌های آزمایشگاهی اخیر استخراج گردید. کیفیت خواب ضعیف به صورت PSQI >5 و خواب‌آلودگی بیش از حد در طول روز به صورت ESS >10 تعریف شد. CBI به صورت پیوسته تحلیل و به طور عملیاتی به دسته‌های ۰-۴۹، ۵۰-۷۴ و ۷۵-۱۰۰ تقسیم شد. سال‌های تجربه کار شیفت شب به صورت متغیر پیوسته و به صورت ≤۵ در مقابل >۵ سال دسته‌بندی شد. رگرسیون لجستیک چندمتغیره برای سن، جنس و BMI تعدیل شد. نتایج: میانگین سن ۲۹٫۹۷ ± ۴٫۴۳ سال و BMI برابر با ۲۴٫۱۶ ± ۳٫۱۹ کیلوگرم بر متر مربع بود. میانگین نمرات PSQI، ESS و CBI به ترتیب ۷٫۰۹ ± ۲٫۹۵، ۹٫۲۰ ± ۳٫۳۹ و ۴۵٫۹۹ ± ۲۰٫۹۵ بود. کیفیت خواب ضعیف در ۹۷ نفر (۶۴٫۷٪)، خواب‌آلودگی بیش از حد در طول روز در ۵۶ نفر (۳۷٫۳٪) و علائم اسکلتی‌عضلانی در ۱۲۶ نفر (۸۴٫۰٪) مشاهده شد. نمرات CBI به صورت ۰-۴۹ در ۸۶ نفر (۵۷٫۳٪)، ۵۰-۷۴ در ۴۸ نفر (۳۲٫۰٪) و ۷۵-۱۰۰ در ۱۶ نفر (۱۰٫۷٪) دسته‌بندی شد. فشار خون در محدوده پرفشاری خون در ۲۱ نفر (۱۴٫۰٪) و پیش‌دیابت در ۱۱ نفر (۷٫۳٪) شناسایی شد. هر سال اضافه تجربه کار شیفت شب با شانس بیشتر کیفیت خواب ضعیف (نسبت شانس تعدیل‌شده (aOR): ۱٫۴۳۳، ۹۵٪ CI: ۱٫۱۱۸-۱٫۸۳۵)، خواب‌آلودگی بیش از حد در طول روز (aOR: ۱٫۲۰۵، ۹۵٪ CI: ۱٫۰۰۱-۱٫۴۵۰)، CBI ≥۵۰ (aOR: ۱٫۸۱۰، ۹۵٪ CI: ۱٫۳۵۰-۲٫۴۲۸)، علائم اسکلتی‌عضلانی (aOR: ۱٫۳۷۲، ۹۵٪ CI: ۱٫۰۰۳-۱٫۸۷۸)، علائم کمر (aOR: ۱٫۲۹۶، ۹۵٪ CI: ۱٫۰۵۱-۱٫۵۹۸)، علائم گردن (aOR: ۱٫۲۷۲، ۹۵٪ CI: ۱٫۰۴۳-۱٫۵۵۲) و فشار خون در محدوده پرفشاری خون (aOR: ۱٫۳۸۷، ۹۵٪ CI: ۱٫۰۵۲-۱٫۸۲۹) همراه بود. ارتباط معناداری با علائم شانه یا پیش‌دیابت مشاهده نشد. نتیجه‌گیری: تکنولوژیست‌های پلی‌سومنوگرافی شیوع بالای کیفیت خواب ضعیف، خواب‌آلودگی بیش از حد در طول روز، علائم مرتبط با فرسودگی شغلی و علائم اسکلتی‌عضلانی را گزارش کردند. سال‌های بیشتر تجربه کار شیفت شب پس از تعدیل برای سن، جنس و BMI با چندین پیامد نامطلوب سلامت همراه بود. این یافته‌ها با توجه به طراحی مقطعی و احتمال وجود عوامل مخدوش‌کننده باقیمانده باید با احتیاط تفسیر شوند. انجام مطالعات آینده‌نگر چندمرکزی توصیه می‌شود.