پیش بینی عوامل موثر بر اختلالات اسکلتی- عضلانی کاربران رایانه دانشگاه علوم پزشکی کرمان به روش شبکه عصبی در سال 1396

ﻣﻘﺪﻣﻪ: در 20 ﺳﺎل اﺧﯿﺮ اﯾﺴﺘﮕﺎهﻫﺎی ﮐﺎر ﺑﺎ راﯾﺎﻧﻪ درﻣﺤﯿﻂﻫﺎی ﮐﺎری و ﻣﻨﺎزل ﻣﺴﮑﻮﻧﯽ اﻓﺰاﯾﺶ ﭼﺸﻤﮕﯿﺮی ﭘﯿﺪا ﮐﺮده ﮐﻪﺑﺎﻋﺚ ﺗﺴﺮﯾﻊ دراﻧﺠﺎم ﮐﺎرﻫﺎ و ﺻﺮﻓﻪﺟﻮﯾﯽ در زﻣﺎن، اﻧﺮژی و ﻣﻨﺎﺑﻊ ﺷﺪه اﺳﺖ. اﻓﺰاﯾﺶ ﮐﺎر ﺑﺎ راﯾﺎﻧﻪ و ﺷﺮاﯾﻂ ﺣﺎﮐﻢﺑﺮ ﻣﺤﯿﻂﻫﺎی ﮐﺎری، اﻧﺴﺎن را درﻣﻌﺮض رﯾﺴﮏﻓﺎﮐﺘﻮرﻫﺎی اﺧﺘﻼﻻت اﺳﮑﻠﺘﯽﻋﻀﻼﻧﯽ از ﻗﺒﯿﻞ ﭘﻮﺳﭽﺮ ﻧﺎﻣﻄﻠﻮب ﯾﺎ ﺑﺤﺮاﻧﯽ اﻧﺪامﻫﺎی ﺑﺪن، ﮐﺎر اﺳﺘﺎﺗﯿﮏ، ﺗﮑﺮار ﻋﻤﻞ، اﻓﺰاﯾﺶ ﻓﻌّﺎﻟﯿّﺖاﺳﺘﺎﺗﯿﮏ ﻣﺎﻫﯿﭽﻪای ﭘﺸﺖو ﺷﺎﻧﻪ ﻗﺮار داده اﺳﺖ ﮐﻪ اﯾﻦ رﯾﺴﮏﻓﺎﮐﺘﻮرﻫﺎ ﺑﺎروش rosa ارزﯾﺎﺑﯽ ﻣﯽﺷﻮﻧﺪ. وزن دﻫﯽ اﯾﻦ رﯾﺴﮏﻓﺎﮐﺘﻮرﻫﺎ ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﺷﺒﮑﻪﻋﺼﺒﯽ اﻧﺠﺎم ﭘﺬﯾﺮﻓﺖ. روش ﮐﺎر: ﻣﻄﺎﻟﻌﻪ ﺑﻪﺻﻮرت ﻣﻘﻄﻌﯽ، در داﻧﺸﮕﺎه ﻋﻠﻮمﭘﺰﺷﮑﯽ ﮐﺮﻣﺎن، ﺑﺮروی 200 اﯾﺴﺘﮕﺎه ﮐﺎری اﻧﺠﺎم ﺷﺪ. اﺑﺘﺪا ﻣﺘﻐﯿﺮﻫﺎی ﻣﺆﺛﺮ ﺑﺮ اﺧﺘﻼﻻت اﺳﮑﻠﺘﯽ-ﻋﻀﻼﻧﯽ ﺑﺎ روش rosa، ﺗﻌﯿﯿﻦ ﺷﺪه، و ﺳﭙﺲ ﻧﻤﺮه ﻫﺮﯾﮏ از آنﻫﺎ ﺗﻌﯿﯿﻦﺷﺪ. ﺳﭙﺲ ﻧﻤﺮه ﻧﻬﺎﯾﯽ اﺧﺘﻼﻻت اﺳﮑﻠﺘﯽ-ﻋﻀﻼﻧﯽ ﮐﺎر ﺑﺎ راﯾﺎﻧﻪ ﺗﻌﯿﯿﻦ و ﭘﺲ از ﭘﯿﺶ ﭘﺮدازش دادهﻫﺎ، ﭘﯿﺶﺑﯿﻨﯽ ﺗﺄﺛﯿﺮ ﻋﻮاﻣﻞ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺒﮑﻪﻋﺼﺒﯽ ﺑﻪدﺳﺖ آﻣﺪ. دادهﻫﺎ ﺑﺎ ﻧﺮم اﻓﺰار 18.0 ibm spss modeler ﺗﺠﺰﯾﻪ-ﺗﺤﻠﯿﻞ ﺷﺪ. ﯾﺎﻓﺘﻪﻫﺎ: ﻣﯿﺎﻧﮕﯿﻦ ﻧﻤﺮه ﻧﻬﺎﯾﯽ rosa، ﺻﻨﺪﻟﯽ، ﺗﻠﻔﻦ-ﻣﺎﻧﯿﺘﻮر و ﻣﻮس-ﮐﯿﺒﻮرد ﺑﻪﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮ 0/91± 4/36 ،3/68 ± 1/09 ،3/67± 1/06 و 3/66±1/18 ﺑﻪدﺳﺖ آﻣﺪ. 131 اﯾﺴﺘﮕﺎهﮐﺎر (65/5%) ﻧﻤﺮه ای ﮐﻤﺘﺮاز 5 و 69 اﯾﺴﺘﮕﺎه (34/5%) ﻧﻤﺮه ای ﺑﺮاﺑﺮ وﺑﺎﻻﺗﺮ از 5 دارﻧﺪ. ﻃﺒﻖ ﻧﺘﺎﯾﺞ ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﻋﺎﻣﻞ ﺻﻨﺪﻟﯽ ﺑﺎ وزن ﻧﺮﻣﺎل ﺷﺪه 41%، ﻋﺎﻣﻞ ﺗﻠﻔﻦ-ﻣﺎﻧﯿﺘﻮر ﺑﺎوزن ﻧﺮﻣﺎلﺷﺪه 31% و ﻧﻬﺎﯾﺘﺎً ﻣﻮس-ﮐﯿﺒﻮرد ﺑﺎ وزنﻧﺮﻣﺎل ﺷﺪه 28% ﺑﻪﺗﺮﺗﯿﺐ ﻋﻮاﻣﻞ ﻣﺆﺛﺮ ﺑﺮاﺧﺘﻼﻻت ﮐﺎر ﺑﺎراﯾﺎﻧﻪ اﺳﺖ. ﻧﺘﯿﺠﻪﮔﯿﺮی: ﺑﯿﺸﺘﺮﯾﻦ وزن ﻋﻮاﻣﻞ ﻣﺆﺛﺮ ﺑﺮاﺧﺘﻼﻻت اﺳﮑﻠﺘﯽ-ﻋﻀﻼﻧﯽ ﮐﺎر ﺑﺎﮐﺎﻣﭙﯿﻮﺗﺮ ﻃﺒﻖ اوﻟﻮﯾﺖﺑﻨﺪی اﻟﮕﻮرﯾﺘﻢ ﺷﺒﮑﻪﻋﺼﺒﯽ ﺑﻪﺗﺮﺗﯿﺐ ﺑﺮاﺑﺮﺻﻨﺪﻟﯽ، ﺳﭙﺲ ﺗﻠﻔﻦ-ﻣﺎﻧﯿﺘﻮر و ﻣﻮس-ﮐﯿﺒﻮرد اﺳﺖ. درﻧﺘﯿﺠﻪ ﺑﺎاﺻﻼح ارﮔﻮﻧﻮﻣﯿﮏ ﺻﻨﺪﻟﯽ وﺟﺎﻧﻤﺎﯾﯽ ﻣﻨﺎﺳﺐ ﺗﻠﻔﻦ وﻣﺎﻧﯿﺘﻮر ﻣﯽﺗﻮان از ﻗﺴﻤﺖ ﻋﻤﺪهای از آﺳﯿﺐﻫﺎ ﺟﻠﻮﮔﯿﺮی ﮐﺮد.

Predicting the Effective Factors on Musculoskeletal Disorders among Kerman University of Medical Sciences Computer Users through Neural Network Algorithm in 2018

Introduction: In the past 20 years, computers and their workplaces have increased at both offices and houses, which consequently has led to saving in time, energy and resources. This study aimed to weight risk factors of musculoskeletal disorders among computer users through neural network. Methods: A crosssectional study was carried out at 200 stations in Kerman University of Medical Sciences. Firstly, the factors affecting musculoskeletal disorders through ROSA were determined, and then the score for each of them was determined. Then, the final score of user's musculoskeletal disorders was determined, and after preprocessing, the prediction of the effect of factors was obtained using neural network. Data was analyzed using IBM SPSS Modeler 18.0. Results: The average of final score of ROSA, chair, telephonemonitor and mousekeyboard were 4.36 ± 0.91, 3.67 ± 1.06, 3.68 ± 1.09 And 3.66 ± 1.18 respectively. 131 Workstation (65.5%) had a score less than 5 69 Workstation (34.5%) had a score equal to or greater than 5. Based on neural network algorithm Chair factor with a normalized weighting 41%; telephonemonitor factor with a normalized weighting 31% and finally mousekeyboard factor with a weighting factor 28% were respectively effective factors on disorders caused by working with computers. Conclusions: The most normalized weight is for chair, and then the telephonemonitor and mousekeyboard. We should include ergonomic interventions considering the effect of each factor (normalized weighting of factors) provided by neural network to decrease such disorders.