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بررسی مقایسه‌ای برنامه آموزشی رشته فلوشیپ انفورماتیک بالینی در دانشگاه‌‌های منتخب جهان A Comparative Study of the Clinical Informatics Fellowship Curriculum in Selected Universities of the World 1 1 مقدمه: انجمن تخصصی پزشکی آمریکا از سال 2011 میلادی جدیدترین رشته فوق‌تخصصی پزشکی را رشته «انفورماتیک بالینی» معرفی کرد. در راستای راه‌اندازی دوره‌های مشابه در کشور می‌بایست، برای طراحی و ارزیابی برنامه آموزشی اقدام گردد. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ایﻦﮐﻪ ﻣﻘﺎیﺴﻪ ﻧﻈﺎم‌ﻫﺎی ﻣﺨﺘﻠﻒ آﻣﻮزﺷﯽ ﺑﺎﻋﺚ ارﺗﻘﺎی ﻣﺤﺘﻮا و ﮐﯿﻔﯿﺖ طراحی یک ﺑﺮﻧﺎﻣﻪ آﻣﻮزﺷﯽ ﻣﯽ‌ﺷﻮد؛ ﻟﺬا ایﻦ ﻣﻄﺎﻟﻌﻪ ﺑﺎ ﻫﺪف ﺷﻨﺎﺳﺎیﯽ و ﻣﻘﺎیﺴﻪ ﺑﺮﻧﺎﻣﻪ آﻣﻮزﺷﯽ انفورماتیک بالینی در چند دانشگاه برتر جهان انجام گردید. روش: مطالعه حاضر یک پژوهش کاربردی است که در سال 1398 انجام پذیرفت. ابزار مورد استفاده چک لیست پژوهشگر ساخته است که بر اساس بررسی متون و نظرات متخصصان در جلسات بحث درون گروهی طراحی گردید. روایی آن توسط هفت نفر از متخصصان حوزه انفورماتیک پزشکی، تخصص‌های بالینی و آموزش پزشکی و پایایی آن توسط روش هم ارزی تأیید شد. روش جمع‌آوری داده‌ها مراجعه به وب سایت دانشگاه‌‌ها به منظور تکمیل چک لیست مطالعه بود. نتایج: در اغلب دانشگاه‌‌های مورد مطالعه رشته فلوشیپ انفورماتیک بالینی بر اساس استانداردهای ACGME اجرا می‌گردید و محتوای اصلی این دوره در قالب چهار موضوع اصول و مفاهیم، تصمیم‌گیری بالینی و ارتقای فرآیند مراقبت، سیستم‌های اطلاعات سلامت، هدایت و مدیریت تغییر بود. نتیجه ­گیری: اعم ﺷﺒﺎﻫﺖ‌ﻫﺎ بین ﺑﺮﻧﺎﻣﻪ‌ﻫﺎ ﺑﻪ طول مدت دوره و فرصت انجام پژوهش‌های مرتبط در زمینه انفورماتیک بالینی و اعم ﺗﻔﺎوت‌ﻫﺎ ﻧﯿﺰ ﺑﯿﺸﺘﺮ ﻣﺮﺑﻮط ﺑﻪ فرصت انجام تدریس در زمینه انفورماتیک بالینی و شرکت فراگیران در برگزاری ژورنال کلاب در طول دوره مربوطه می‌باشد. 2 Introduction: The American Medical Association has introduced the newest medical subspecialty field known as "Clinical Informatics" since 2011. In order to present similar courses in Iran, it is necessary to design and evaluate a training program. The comparison of educational systems improves the content and quality of design of an educational program. Therefore, the objective of this study was to identify and compare the clinical informatics curriculum in several top universities in the world. Method: The present study was an applied one conducted in 2019. The tool used in this study was a researcher-made checklist that was designed based on reviewing the texts and opinions of experts in group discussion sessions. Its validity was confirmed by seven experts in medical informatics, clinical specialties, and medical education and its reliability was confirmed using the equivalence method. The data were collected by referring to the university websites to complete the study checklist. Results: In most of the studied universities, the clinical informatics fellowship course was implemented according to ACGME standards and the main content of this course included four main topics: principles and concepts, clinical decision-making and improving the care process, health information systems, and guiding and managing change. Conclusion: The similarities were mostly related to the duration of the course and the opportunity to conduct relevant researches in the field of clinical informatics. Most of the differences were also related to the opportunity to teach clinical informatics and learners' participation in holding a journal club during the course. 232 241 2020/10/31 1399/8/10 2020/11/3 1399/8/13 رقیه ارشاد سرابی Roghayeh Ershad Sarabi دکترای تخصصی مدیریت اطلاعات سلامت، استادیار، گروه علوم اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران a.ershadsarabi@gmail.com نوشین کهن Nooshin Kohan دکترای تخصصی آموزش پزشکی، استادیار، گروه آموزش پزشکی، دانشگاه علوم پزشکی مجازی، تهران، ایران ایران nu.kohan@gmail.com سارا شفیعان Sara Shafian دانشجوی دکترای تخصصی آموزش پزشکی، دانشکده مجازی مدیریت و آموزش پزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران ایران hajarshafian@gmail.com فائزه افضلی Faezeh Afzali کارشناس ارشد فناوری اطلاعات سلامت، دفتر تحقیق و توسعه انفورماتیک بالینی، واحد توسعه تحقیقات بالینی، مرکز آموزشی درمانی شفا، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران f.afzali90@gmail.com فرهاد فاتحی Farhad Fatehi دکترای تخصصی انفورماتیک پزشکی، دانشیار، مرکز سلامت آنلاین، دانشگاه کوئینزلند، بریزبن، استرالیا استرالیا ffatehi@gmail.com افشین صرافی نژاد Afshin Sarafi Nejad دکترای تخصصی انفورماتیک پزشکی، استادیار، گروه علوم اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دفتر تحقیق و توسعه انفورماتیک بالینی، واحد توسعه تحقیقات بالینی، مرکز آموزشی درمانی شفا، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران a.sarafi@gmail.com Comparative Study Clinical Informatics Medical Informatics Medical Education مطالعه تطبیقی انفورماتیک بالینی انفورماتیک پزشکی آموزش پزشکی 1.Institute of Medicine (US) Committee on Data Standards for Patient Safety, Aspden P, Corrigan JM, Wolcott J, Erickson SM. Patient Safety: Achieving a New Standard for Care. Washington (DC): National Academies Press (US); 2004. doi: 10.17226/10863##2. Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. Institute of Medicine; 2000.##3. Finnell J, Dixon BE. Clinical Informatics Study Guide: Text and Review (Health Informatics). 1st ed. NewYork: Springer; 2016.##4. Kron K, Myers S, Volk L, Nathan A, Neri P, Salazar A, et al. Incorporating medication indications into the prescribing process. Am J Health Syst Pharm 2018;75(11):774-83. doi: 10.2146/ajhp170346##5. Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003;348(25):2526-34. doi: 10.1056/NEJMsa020847##6. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003;163(12):1409-16. doi: 10.1001/archinte.163.12.140##7. Rahimi B, Safdari R, Jebraeily M. Development of hospital information systems: user participation and factors affecting it. Acta Inform Med 2014;22(6):398-401. doi: 10.5455/aim.2014.22.398-401##8. Zimlichman E, Rozenblum R, Salzberg CA, Jang Y, Tamblyn M, Tamblyn R, Bates DW. Lessons from the Canadian national health information technology plan for the United States: opinions of key Canadian experts. J Am Med Inform Assoc 2012;19(3):453-9. doi: 10.1136/amiajnl-2011-000127##9. Lehmann CU, Shorte V, Gundlapalli AV. Clinical informatics sub-specialty board certification. Pediatr Rev 2013;34(11):525-30. doi: 10.1542/pir.34-11-525##10. Alexander E, Karipineni N, Landman A. Clinical Informatics. Brigham and Women's Hospital: Harvard Medical School; 2020.##11. Stanford Medicine. Stanford Clinical Informatics Fellowship [cited 2020 Oct 30]. Available from: http://med.stanford.edu/cifellowship.html.##12. Informatics Areas: Clinical Informatics [cited 2020 Oct 30]. Available from: https://www.amia.org/applications-informatics/clinical-informatics##13. Cimino JJ, Shortliffe EH. Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics). NewYork: Springer; 2006.##14. Lehmann CU, Longhurst C, Hersh W, Mohan V, Levy B, Embi PJ, et al. Clinical informatics fellowship programs: in search of a viable financial model: an open letter to the Centers for Medicare and Medicaid Services. Applied Clinical Informatics 2015;6(2):267.##15. ACGME Program Requirements for Graduate Medical Education in Clinical Informatics [cited 2020 Oct 30]. Available from: https://www.acgme.org/Portals/0/PFAssets/ProgramRequirements/381_ClinicalInformatics_2020.pdf?ver=2020-06-29-163724-707##16. UCLA Health. Clinical Informatics Fellowship. [cited 2020 Oct 30]. Available from: https://www.uclahealth.org/clinical-informatics-fellowship#discipline.##17. Clinical Informatics Fellowship [cited 2020 Oct 30]. Available from: https://www.dbmi.columbia.edu/clinical-informatics-fellowship/##18. College of Medicinie Phoenix. Biomedical Informatics - Clinical Fellowship Program Curriculum. [cited 2020 Oct 30]. Available from: https://phoenixmed.arizona.edu/clinical-informatics-fellowship##19. Columbia University Medical Center (CUIMC). Clinical Informatics Fellowship [cited 2020 Oct 30]. Available from: https://www.dbmi.columbia.edu/clinical-informatics-fellowship.##20. Mayoclinic. College of Medicinie an science. Clinical Informatics Fellowship (Minnesota) [cited 2020 Oct 30]. Available from: https://college.mayo.edu/academics/residencies-and-fellowships/clinical-informatics-fellowship-minnesota/.##21. OHSU. Department of Medical Informatics and Clinical Epidemiology. Clinical Informatics Subspecialty Fellowship [cited 2020 Oct 30]. Available from: https://www.ohsu.edu/school-of-medicine/medical-informatics-and-clinical-epidemiology/clinical-informatics-subspecialty.##22. Pereira AG, Kim M, Seywerd M, Nesbitt B, Pitt MB; Minnesota Epic101 Collaborative. Collaborating for Competency-A Model for Single Electronic Health Record Onboarding for Medical Students Rotating among Separate Health Systems. Appl Clin Inform 2018;9(1):199-204. doi:10.1055/s-0038-1635096.##23. Residencies and Fellowships; Clinical Informatics Fellowship (Minnesota). College of Medicinie and Science. Mayo Clinic [cited 2020 Oct 30]. Available from: https://college.mayo.edu/academics/residencies-and-fellowships/clinical-informatics-fellowship-minnesota/curriculum/.##24. Kahooei M, Soleimani M, Ghazavi S, Alaei S. Views, behavior and satisfaction of the nurses and other hospital ward personnel about the effectiveness of computer systems of hospital information on caring process. Health Information Management 2007; 4(2): 193-202. [In Persian]##25. Electronic Health Roadmap; Iranian Ministry of Health and Medical Education [cited 2020 Oct 30]. Available from: http://cim.kmu.ac.ir/Images/Article/32810/moh-ehealth-roadmap-07-web.pdf Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003;348(25):2526-34. doi: 10.1056/NEJMsa020847## ##

طراحی و ایجاد برنامه کاربردی آموزش مبتنی بر تلفن همراه برای اختلال طیف اوتیسم Design and Development of an Educational Mobile Application for Autism Spectrum Disorder 1 1 مقدمه: اوتیسم از شایع ترین اختلالات عصبی رشدی می­باشد که به دلیل آشنا نبودن والدین با این اختلال دیر تشخیص داده می‌شود. عدم آگاهی و شناخت والدین یکی از مشکلات مهم این بیماری می‌باشد هدف این مطالعه ایجاد برنامه کاربردی آموزشی مبتنی بر تلفن همراه هوشمند برای اختلال طیف اوتیسم ویژه والدین بود. روش: این پژوهش از نوع توسعه‌ای-کاربردی بود که در سه مرحله انجام شد. ابتدا پرسشنامه تنظیم شده جهت نیازسنجی اطلاعاتی و تعیین عناصر داده و قابلیت‌های برنامه کاربردی توسط پزشکان متخصص روانپزشک کودک و مغز و اعصاب اطفال در بیمارستان‌های دانشگاه علوم پزشکی ایران تکمیل گردید. داده‌های به دست آمده تحلیل و برنامه کاربردی بر پایه نتایج طراحی شد و در نهایت جهت ارزیابی کاربردپذیری و رضایت کاربران در اختیار 34 نفر از والدین قرار گرفت. نتایج: پزشکان متخصص، اکثر عناصر داده‌ای پرسشنامه را ضروری تشخیص دادند. قابلیت‌های اصلی برنامه کاربردی مانند آموزش علائم و نشانه‌های اوتیسم، راهکارهای خودمراقبتی از اوتیسم و آزمون غربالگری تشخیص اوتیسم برای سنین 6، 12، 24 و 36 ماهگی بود. ارزیابی کاربردپذیری نشان داد که والدین، برنامه کاربردی را با میانگین امتیاز 7/6 (از مجموع 9 امتیاز) در سطح خوب ارزیابی کردند. نتیجه‌گیری: برنامه کاربردی آموزش مبتنی بر تلفن همراه در زمینه اوتیسم با آموزش والدین به منظور تشخیص، خودمراقبتی و توانبخشی از اوتیسم می‌توانند به تشخیص و مداخلات درمانی زودهنگام اوتیسم کمک کرده و ابزاری در دسترس برای آموزش والدین باشند. آموزش تشخیص، خودمراقبتی و تعامل با کودک اوتیستیک به صورت یک ابزار در دسترس مورد رضایت والدین قرار دارد. 2 Introduction: Autism is one of the most common neurodevelopmental disorders that is diagnosed late due to parents' unfamiliarity with the disorder. Lack of knowledge of parents is one of the major problems of this disorder. The objective of this study was to develop an educational smartphone-based application for autism spectrum disorder specific for parents. Method: This study was an applied-developmental one conducted in three phases. First, a questionnaire designed for assessing information needs as well as determining data elements and application features was completed by specialists in the fields of pediatric psychology and pediatric neurology in hospitals affiliated to Iran University of Medical Sciences. The data were analyzed and the application was designed based on the results and finally, in order to evaluate the usability and user satisfaction, it was presented to 34 parents. Results: The majority of the data elements were regarded as necessary by specialists. The main features of the application included training the signs and symptoms of autism, self-care approaches, and diagnostic screening test for the ages of 6, 12, 24, and 36 months. Usability evaluation showed that parents evaluated the application as good with the mean score of 7.6 (out of 9). Conclusion: Educational mobile application for autism specific for parents, in terms of diagnosis, self-care, and rehabilitation, could be helpful for early diagnosis and treatment of autism and accessible for training parents. Training diagnosis, self-care, and interaction with the autistic child was approved as an accessible tool by parents.  242 251 2020/10/312019/10/8 1398/7/16 2020/11/32020/01/8 1398/10/18 مصطفی لنگری زاده Mostafa Langarizadeh دکتری تخصصی انفورماتیک پزشکی، دانشیار، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران Langarizadeh.m@iums.ac.ir محمد حیدری Mohammad Heydari کارشناس ارشد فناوری اطلاعات سلامت، مربی، گروه فناوری اطلاعات سلامت، دانشکده علوم پزشکی خلخال، خلخال، ایران ایران heydari.mohammad12@yahoo.com میترا حکیم شوشتری Mitra Hakim Shooshtari فوق تخصص روانپزشکی کودک و نوجوان، دانشیار‌، دانشگاه علوم پزشکی ایران، دانشکده پزشکی، مرکز تحقیقات سلامت روان، تهران، ایران ایران mitra.hakim@gmail.com Autism Spectrum Disorder Parents Education Application Mobile Phone اختلال طیف اوتیسم والدین آموزش برنامه کاربردی تلفن همراه 1. Ramtekkar UP. DSM-5 Changes in Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder: Implications for Comorbid Sleep Issues. Children (Basel) 2017;4(8):62. doi: 10.3390/children4080062##2. Newschaffer CJ, Croen LA, Daniels J, Giarelli E, Grether JK, Levy SE, et al. The epidemiology of autism spectrum disorders. Annu Rev Public Health 2007;28:235-58. doi: 10.1146/annurev.publhealth.28.021406.144007##3. Mattila ML, Kielinen M, Linna SL, Jussila K, Ebeling H, Bloigu R, et al. Autism spectrum disorders according to dsm-iv-tr and comparison with dsm-5 draft criteria: An epidemiological study. J Am Acad Child Adolesc Psychiatry 2011;50(6):583-92.e11. doi: 10.1016/j.jaac.2011.04.001##4. Kim YS, Leventhal BL, Koh YJ, Fombonne E, Laska E, Lim EC, et al. Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry 2011;168(9):904-12. doi: 10.1176/appi.ajp.2011.10101532##5. Elsabbagh M, Divan G, Koh YJ, Kim YS, Kauchali S, Marcin C, et al. Global prevalence of autism and other pervasive developmental disorders. Autism Res 2012;5(3):160-79. doi: 10.1002/aur.239##6. Saemundsen E, Ludvigsson P, Rafnsson V. Autism spectrum disorders in children with a history of infantile spasms: A population-based study. J Child Neurol 2007;22(9):1102-7. doi: 10.1177/0883073807306251##7. Vahia VN. Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian J Psychiatry 2013;55(3):220-3. doi: 10.4103/0019-5545.117131##8. Mohammadijahromi L, Inaloo S, Raeesi H. Prevalence of autism among epileptic children referring to imam reza clinic and comparing to control group (non epileptic children) during2013-2014. The Journal of Shahid Sadoughi University of Medical Sciences. 2017;25(3):162-70. [In Persian]##9. Kim SK. Recent update of autism spectrum disorders. Korean J Pediatr 2015;58(1):8-14. doi: 10.3345/kjp.2015.58.1.8##10. Dawson G. Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev Psychopathol 2008;20(3):775-803. doi: 10.1017/S0954579408000370##11. Courchesne E, Pierce K, Schumann CM, Redcay E, Buckwalter JA, Kennedy DP, et al. Mapping early brain development in autism. Neuron 2007;56(2):399-413. doi: 10.1016/j.neuron.2007.10.016##12. McConachie H, Diggle T. Parent implemented early intervention for young children with autism spectrum disorder: A systematic review. J Eval Clin Pract 2007;13(1):120-9. doi: 10.1111/j.1365-2753.2006.00674.x##13. Towle PO, Patrick PA. Autism spectrum disorder screening instruments for very young children: A systematic review. Autism Res Treat 2016;2016:4624829. doi: 10.1155/2016/4624829##14. Vakilizadeh N, Abedi A, Mohseni Ezhiyeh A, Pishghadam E. Effectiveness of Family-Based Early Intervention on the Degree of Joint Attention (Responding) of the Children with Autism Spectrum Disorder: A Single-Subject Study. Archives of Rehabilitation 2016;17(1):42-53. [In Persian] doi: 10.20286/jrehab-170140##15. Oono IP, Honey EJ, McConachie H. Parent-mediated early intervention for young children with autism spectrum disorders (asd). Cochrane Database Syst Rev 2013; 21(4): 97-74. doi: 10.1002/14651858.CD009774.pub2##16. Ingersoll B, Gergans S. The effect of a parent-implemented imitation intervention on spontaneous imitation skills in young children with autism. Res Dev Disabil 2007;28(2):163-75. doi: 10.1016/j.ridd.2006.02.004##17. Catalano D, Holloway L, Mpofu E. Mental Health Interventions for Parent Carers of Children with Autistic Spectrum Disorder: Practice Guidelines from a Critical Interpretive Synthesis (CIS) Systematic Review. Int J Environ Res Public Health 2018;15(2):341. doi: 10.3390/ijerph15020341##18. Bearss K, Burrell TL, Stewart L, Scahill L. Parent Training in Autism Spectrum Disorder: What's in a Name? Clin Child Fam Psychol Rev 2015;18(2):170-82. doi: 10.1007/s10567-015-0179-5 ##19. Iadarola S, Levato L, Harrison B, Smith T, Lecavalier L, Johnson C, et al. Teaching parents behavioral strategies for autism spectrum disorder (asd): Effects on stress, strain, and competence. J Autism Dev Disord 2018; 48(4):131-40. doi: 10.1007/s10803-017-3339-2##20. Li WH, Hu LF, Yuan L, Hao G, Zhu ZW. The application of the first year inventory for ASD screening in china. Journal of Pediatric Nursing 2019;44(7):72-8. https://doi.org/10.1016/j.pedn.2018.11.004##21. Kanne SM, Carpenter LA, Warren Z. Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile-health screening tool. Autism Res 2018;11(7):1038-49. doi: 10.1002/aur.1959##22. Silva BMC, Rodrigues JJ, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: A review of current state in 2015. J Biomed Inform 2015;56:265-72. doi: 10.1016/j.jbi.2015.06.003##23. Martinez-Perez B, de la Torre-Diez I, Lopez-Coronado M. Mobile health applications for the most prevalent conditions by the world health organization: Review and analysis. J Med Internet Res 2013;15(6):e120. doi: 10.2196/jmir.2600##24. Kao CW, Chuang HW, Chen TY. The utilization of health-related applications in chronic disease self-management. Hu Li Za Zhi 2017;64(4):19-25. [In Chinese] doi: 10.6224/JN.000050##25. Lewis TL, Boissaud-Cooke MA, Aungst TD, Eysenbach G. Consensus on use of the term “app” versus “application” for reporting of mhealth research. J Med Internet Res 2014; 16(7): e174. doi: 10.2196/jmir.3460##26. Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med 2015;17(3):283-221. doi: 10.1126/scitranslmed.aaa3487##27. Langarizadeh M, Shsooshtari MH, Mehraeen E, Heydari M. Data Requirements and Technical Capabilities of Educational Mobile Application for ‎Parents with Autistic Child. Journal of Paramedical Sciences and Rehabilitation 2020;9(3):81071.[In Persian] doi: 10.22038/JPSR.2020.47186.2090##28. Bailey JE. Development of an instrument for the management of computer user attitudes in hospitals. Methods Inf Med 1990;29(1):51-6.##29. Chin J, Diehl V, Norman K. Development of an instrument measuring user satisfaction of the human-computer interface. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 1998 May; New York: Association for Computing Machinery; 2018. p. 213-18. https://doi.org/10.1145/57167.57203##30. Lefer G, Rouches A, Bourdon P, Lopez Cazaux S. Training children with autism spectrum disorder to undergo oral assessment using a digital ipad application. Eur Arch Paediatr Dent 2019;20(2):113-21. doi: 10.1007/s40368-018-0398-9##31. Khodabakhshi Koolaee A, Arab Varamin A, Rooshan Cheseli R. Effectiveness of Kozloff Parental Training Program on Expressed Emotion and Burden Among Mothers With Autistic Child. 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استفاده از تکنیک‌های داده‌کاوی جهت تشخیص هوشمند شدت اختلال افسردگی Using Data Mining Techniques for Intelligent Diagnosis of Severity of Depressive Disorder 1 1 مقدمه: پیاده‌سازی روشی که بتواند هر فرد را در تشخیص یا پیشگیری اختلالات روانی یاری رساند می‌تواند گامی مهم در جهت پیشگیری و کنترل این اختلالات به خصوص در مراحل ابتدایی آن‌ها تلقی شود. هدف پژوهش حاضر به‌کار‌گیری تکنیک‌های داده‌کاوی در تشخیص هوشمند شدت اختلال افسردگی است. روش: پژوهش کاربردی حاضر با مراجعه به تعدادی کلینیک روانپزشکی در شهر تهران و بررسی پرونده بیماران انجام شد. 420 نفر که به پرسشنامه مینه سوتا 71 سؤالی پاسخ داده بودند با استفاده از روش نمونه‌گیری در دسترس به عنوان نمونه انتخاب شدند (300 نفر مبتلا به مراتب متفاوتی از افسردگی و 120 نفر فاقد آن). پاسخنامه آزمون مینه‌سوتا 71 سؤالی و تشخیص روانپزشک به عنوان داده برای ایجاد مدل توسط الگوریتم‌های K نزدیک‌ترین همسایه، درخت تصمیم و ماشین بردار پشتیبان مورد استفاده قرار گرفت. 70 درصد داده‌ها برای آموزش و 30 درصد داده‌ها برای اعتبار‌سنجی مدل به کار گرفته شد. جهت تحلیل داده‌ها از نرم‌افزار Matlab استفاده شد. نتایج: نتایج ارزیابی‌ها نشان داد که الگوریتم درخت تصمیم با دقت 99/16 درصد به دقت بالا‌تری در مقایسه با دیگر الگوریتم‌ها دست یافت. همچنین با اجرای مدل‌های ایجاد شده بر روی هر سؤال آزمون مینه سوتا 71 سؤالی تأثیر هر سؤال در ارزیابی مشخص شد. نتیجه‌گیری: تقسیم‌بندی بیماران با رویکرد داده‌کاوی و بر اساس مهم‌ترین ویژگی‌ها، می‌تواند ابزار مفید و مؤثر برای تحلیل و بهبود فرآیند تصمیم‌گیری پزشکان در رابطه با درمان بیماران باشد. 2 Introduction: Implementing a method that can help individuals diagnose or prevent mental disorders can be an important step in preventing and controlling these disorders especially in the early stages. The objective of this research was to apply data mining techniques for intelligent diagnosis of severity of depressive disorder. Method: The present applied research was carried out by going to a number of psychiatric clinics in Tehran and investigating patients' medical records. A total of 420 subjects who responded to the Minnesota Multiphasic Personality Inventory (MMPI, 71 questions) were selected through convenience sampling as the sample of the study (300 subjects were diagnosed with different degrees of depression and 120 subjects showed no symptoms of depression). The answer sheet of MMPI and the diagnosis of the psychiatrist were used as data for developing the model by k--Nearest Neighbor (k-NN), Decision Tree, and Support Vector Machine algorithms. About 70 percent of the data were applied for training and 30 percent of the data were used for validating the model. MATLAB software was used for data analysis.  Results: The results of the evaluations showed that Decision Tree algorithm with accuracy of 99.16% had higher accuracy compared to other algorithms. Furthermore, by implementing the developed models on each question of MMPI, the influence of each question on evaluation was determined. Conclusion: Classifying patients with data mining approach and based on the most important characteristics can be a useful and effective tool for analyzing and improving the decision-making process of physicians regarding the treatment of patients. 252 262 2020/10/312019/10/82019/07/14 1398/4/23 2020/11/32020/01/82020/02/3 1398/11/14 فرشته پارساپور Fereshteh Parsapour کارشناسی ارشد روانشناسی بالینی، گروه روانشناسی بالینی، واحد کرج، دانشگاه آزاد اسلامی، البرز، ایران ایران fparsapour1@gmail.com جاوید پیمانی Javid Peymani دکتری تخصصی علوم اعصاب، استادیار، گروه روان‌شناسی بالینی، واحد کرج، دانشگاه آزاد اسلامی، البرز، ایران ایران Dr.Peymani@yahoo.com Depression Intelligent Diagnosis Data Mining افسردگی تشخیص هوشمند داده‌کاوی 1. Bakhtiyari M, Esmaeilpuor M, Ebrahimi ME. Diagnose of Depression Using Artificial Intelligence. 6th Congress of Iranian Psychological Association; 2017 Nov 13-15; Tehran: Iranian Psychological Association; 2017. [In Persian].##2. Babbie ER. The Practice of Social Research. Translated by Faazel R. Tehran: Samt; 2016. [In Persian].##3. Rafiepoor F. Searches and thoughts. Tehran: publication Joint-stock company; 2015. [In Persian].##4. Mukherjee S, Ashish K, Hui N, Chattopaadhyay S. Modeling Depression Data: Feed Forward Neural Network vs Radial Basis Function Neural Network. American Journal of Biomedical Science 2014; 6(3): 166-74. doi: 10.5099/aj140300166##5. Rowley R. Can Artificial Intelligence help in the treatment of depression? 2016 cited 2016 Nov 8. Available from: https://flowhealth.com/blog/2016/11.##6. Jooriyan N, Ashoori M. Predicting the effectiveness of preeclampsia medications based on dose and method of drug consumption using data mining. Iranian Journal of Obstetrics, Gynecology and Infertility 2014; 17(123): 13-22. [In Persian]##7. Tortajada S, García-Gomez JM, Vicente J, Sanjuán J, de Frutos R, Martín-Santos R, et al. Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf Med 2009;48(3):291-8. doi: 10.3414/ME0562##8. Malekpour S, Esmaeilpour M. Investigating Effective Factors on Depression in Individuals Using Data Mining. 2nd International Conference on Knowledge-Based Research in Computer Engineering & IT; 2017 Sep 21; Tehran: Kharazmi University; 2017. [In Persian].##9. Sau A, Bhakta I. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India. J Clin Diagn Res 2017;11(5):VC01-VC04. doi: 10.7860/JCDR/2017/23656.9762##10. Tire H, Jafari H, Farbod D. Depression prediction in students using decision tree algorithm. Summary of Articles of the 12th Annual Research Students Congress of the Eastern Medical Sciences Universities; 2017 Nov 29; Gonabad: Gonabad University of Medical Sciences; 2017. [In Persian].##11. Hooman HA. Structural Equation Modeling Using LISREL Software. Tehran: Samt; 2005. [In Persian]##12. Khodayary‌fard M, Parand A. Psychological Assessment and Testing. Tehran: Tehran University Press; 2009. [In Persian].##13. Groth-Marnat G, Wright AJ. Handbook of Psychological Assessment. Translated by Pasha Sharif H, Nikkhoo MR. Tehran: Sokhan, Roshd; 2010. [In Persian].##14. Han J, Kamber M. Data Mining. Southeast Asia: Morgan Kaufmann; 2006.##15. Fang X. Are you becoming a diabetic? a data mining approach. Sixth International Conference on Fuzzy Systems and Knowledge Discovery; 2009 Aug 14-16; Tianjin, China: IEEE; 2009. doi: 10.1109/FSKD.2009.807##16. Kohavi R, Provost F. Glossary of terms, editorial for the special issue on applications of machine learning and the knowledge discovery process. Machine Learning 1998; 30(2-3): 271-4. https://doi.org/10.1023/A:1017181826899##17. Alizadeh‌Sani R. Diagnosis of heart disease using data mining [dissertation]. Tehran: Sharif University of Technology; 2012. [In Persian##18. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 3rd ed. USA: Morgan Kaufman; 2011.##19. SPSS. Clementine12 User Manual. Chicago: IL; 2007.##20. Mukherjee S, Ashish K, Baran Hui N, Chattopadhyay S. Modeling depression data: feed forward neural network vs. radial basis function neural network. Am J Biomed Sci 2014;6(3):166-74. doi: 10.5099/aj140300166##21. Suhara Y, Xu Y, Pentland A DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. 26th International Conference on World Wide Web; 2017 Apr 3–7; Perth, Australia: ACM; 2017. p. 715–24. doi: 10.1145/3038912.3052676##22. Osubor VI, Egwali AO. A neuro fuzzy approach for the diagnosis of postpartum depression disorder. Iran Journal of Computer Science 2018; 1: 217–25.##1. Bakhtiyari M, Esmaeilpuor M, Ebrahimi ME. Diagnose of Depression Using Artificial Intelligence. 6th Congress of Iranian Psychological Association; 2017 Nov 13-15; Tehran: Iranian Psychological Association; 2017. [In Persian].##2. Babbie ER. The Practice of Social Research. Translated by Faazel R. Tehran: Samt; 2016. [In Persian].##3. Rafiepoor F. Searches and thoughts. Tehran: publication Joint-stock company; 2015. [In Persian].##4. Mukherjee S, Ashish K, Hui N, Chattopaadhyay S. Modeling Depression Data: Feed Forward Neural Network vs Radial Basis Function Neural Network. American Journal of Biomedical Science 2014; 6(3): 166-74. doi: 10.5099/aj140300166##5. Rowley R. Can Artificial Intelligence help in the treatment of depression? 2016 cited 2016 Nov 8. Available from: https://flowhealth.com/blog/2016/11.##6. Jooriyan N, Ashoori M. Predicting the effectiveness of preeclampsia medications based on dose and method of drug consumption using data mining. Iranian Journal of Obstetrics, Gynecology and Infertility 2014; 17(123): 13-22. [In Persian]##7. Tortajada S, García-Gomez JM, Vicente J, Sanjuán J, de Frutos R, Martín-Santos R, et al. Prediction of postpartum depression using multilayer perceptrons and pruning. Methods Inf Med 2009;48(3):291-8. doi: 10.3414/ME0562##8. Malekpour S, Esmaeilpour M. Investigating Effective Factors on Depression in Individuals Using Data Mining. 2nd International Conference on Knowledge-Based Research in Computer Engineering & IT; 2017 Sep 21; Tehran: Kharazmi University; 2017. [In Persian].##9. Sau A, Bhakta I. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India. J Clin Diagn Res 2017;11(5):VC01-VC04. doi: 10.7860/JCDR/2017/23656.9762##10. Tire H, Jafari H, Farbod D. Depression prediction in students using decision tree algorithm. Summary of Articles of the 12th Annual Research Students Congress of the Eastern Medical Sciences Universities; 2017 Nov 29; Gonabad: Gonabad University of Medical Sciences; 2017. [In Persian].##11. Hooman HA. Structural Equation Modeling Using LISREL Software. Tehran: Samt; 2005. [In Persian]##12. Khodayary‌fard M, Parand A. Psychological Assessment and Testing. Tehran: Tehran University Press; 2009. [In Persian].##13. Groth-Marnat G, Wright AJ. Handbook of Psychological Assessment. Translated by Pasha Sharif H, Nikkhoo MR. Tehran: Sokhan, Roshd; 2010. [In Persian].##14. Han J, Kamber M. Data Mining. Southeast Asia: Morgan Kaufmann; 2006.##15. Fang X. Are you becoming a diabetic? a data mining approach. Sixth International Conference on Fuzzy Systems and Knowledge Discovery; 2009 Aug 14-16; Tianjin, China: IEEE; 2009. doi: 10.1109/FSKD.2009.807##16. Kohavi R, Provost F. Glossary of terms, editorial for the special issue on applications of machine learning and the knowledge discovery process. Machine Learning 1998; 30(2-3): 271-4. https://doi.org/10.1023/A:1017181826899##17. Alizadeh‌Sani R. Diagnosis of heart disease using data mining [dissertation]. Tehran: Sharif University of Technology; 2012. [In Persian##18. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 3rd ed. USA: Morgan Kaufman; 2011.##19. SPSS. Clementine12 User Manual. Chicago: IL; 2007.##20. Mukherjee S, Ashish K, Baran Hui N, Chattopadhyay S. Modeling depression data: feed forward neural network vs. radial basis function neural network. Am J Biomed Sci 2014;6(3):166-74. doi: 10.5099/aj140300166##21. Suhara Y, Xu Y, Pentland A DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. 26th International Conference on World Wide Web; 2017 Apr 3–7; Perth, Australia: ACM; 2017. p. 715–24. doi: 10.1145/3038912.3052676##22. Osubor VI, Egwali AO. A neuro fuzzy approach for the diagnosis of postpartum depression disorder. Iran Journal of Computer Science 2018; 1: 217–25. ##

نرم‌افزار آزمون دونیمه کردن خط برای ارزیابی عملکردهای دیداری- فضایی بیماران اسکیزوفرنیا Line Bisection Test Software to Evaluate the Visual-spatial Functions in Schizophrenic Patients 1 1 مقدمه: یکی از روش‌های ارزیابی عملکردهای دیداری-فضایی استفاده از آزمون دونیمه کردن خط است. با توجه به پیشرفت فناوری اطلاعات، در این پژوهش تلاش شد تا نرم‌افزار آزمون دو نیمه کردن خط طراحی و کارایی آن در مقایسه با فرم متداول کاغذ-مدادی آن در بیماران اسکیزوفرنیا بررسی شود. روش: روش مطالعه علی-مقایسه‌ای بود و در ابتدا نرم‌افزار آزمون دو نیمه کردن خط، با برنامه Visual Studio 2015 و دقیقاً مشابه فرم کاغذ-مدادی طراحی شد. در مرحله اول30 نفر از افراد سالم با توجه به ملاک‌های ورود به مطالعه، به روش هدفمند انتخاب شده و از هریک، 3 بار آزمون نرم‌افزاری و 3 بار آزمون کاغذ-مدادی گرفته شد و میانگین انحراف‌ها از نقطه وسط خط محاسبه شد. سپس نتایج در دو آزمون مورد مقایسه قرار گرفت. در مرحله بعد به منظور آزمون عملکرد دیداری-فضایی بیماران، 15 بیمار اسکیزوفرنیا به روش تصادفی از بیماران بستری در بیمارستان روانپزشکی انتخاب شدند و از هر بیمار دو فرم نرم افزاری و کاغذ-مدادی گرفته شد. نتایج: با استفاده از آزمون تحلیل واریانس یکراهه، میانگین انحراف از میانگین در افراد سالم و بیماران اسکیزوفرنیا بررسی شد و نتایج نشان داد بین دو فرم نرم‌افزاری و کاغذ-مدادی تفاوت معناداری وجود ندارد (0/05P<). با توجه به دقت بالای اجرای نرم‌افزاری، خطاهای ناشی از پاسخگویی تصادفی قابل تشخیص توسط نرم‌افزار است. نتیجه‌گیری: آزمون دونیمه کردن خط نرم‌افزاری جایگزین مناسبی برای آزمون مداد کاغذی جهت ارزیابی عملکردهای دیداری فضایی بیماران اسکیزوفرنیا است و می‌توان از این نرم‌افزار در ارزیابی عملکردهای شناختی، مخصوصاً آسیب‌های لوب آهیانه‌ای بیماران اسکیزوفرنیا استفاده کرد. 2 Introduction: One way to evaluate visual-spatial functions is to use line bisection test. With the advancement of information technology, this study attempted to design a line bisection test software and investigate its efficacy compared to the conventional paper-pencil form in schizophrenic patients. Method: The present study was a causal-comparative one and line bisection test software was designed with visual studio 2015 exactly the same as the paper-pencil form. In the first stage, 30 healthy individuals were selected by purposive method according to the inclusion criteria and each of them took 3 software tests and 3 paper-pencil tests and the mean deviations were calculated from the midpoint of the line. Then, the results of the two tests were compared. In the next stage, to test visual-spatial performance, 15 schizophrenic patients were randomly selected from hospitalized psychiatric patients and both software and paper-pencil forms of line bisection test were performed for each patient. Results: Using one-way analysis of variance, the mean deviation from the mean in healthy individuals and schizophrenic patients was evaluated and the results showed no significant difference between software and paper-pencil forms (P<0.05). Due to the high accuracy of software implementation, the errors due to randomized responses are detectable by the software. Conclusion: The results of this study indicated that line bisection test software is a good alternative to paper pencil test to evaluate the spatial-visual performance of schizophrenic patients and this software can be used to evaluate cognitive functions, especially the parietal lobe injuries of schizophrenic patients. 263 272 2020/10/312019/10/82019/07/142019/10/29 1398/8/7 2020/11/32020/01/82020/02/32020/04/18 1399/1/30 الهام طباطبایی کاشانی Elham Tabatabaei Kashani دانشجوی دکتری رشته روانشناسی عمومی، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران ایران elham_taba@yahoo.com الهام فروزنده Elham Foroozandeh استادیار روانشناسی، دانشکده روان‌شناسی، واحدنایین، دانشگاه آزاد اسلامی، نایین، ایران ایران elham_for@yahoo.com ایوب باقری Ayoub Bagheri استادیار آمار کاربردی، گروه روش و آمار، دانشگاه اوترخت، اوترخت، هلند هلند ayoub.bagheri@gmail.com Line bisection test Schizophrenia Software design آزمون دونیمه کردن خط اسکیزوفرنیا طراحی نرم افزار 1. 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Reduced prefrontal activation during Tower of London in first-episode schizophrenia: a multi-channel near-infrared spectroscopy study. Neuroscience Letters 2010;478(3):136-40. https://doi.org/10.1016/j.neulet.2010.05.003##22. Scarpina F, Tagini S. The stroop color and word test. Frontiers in Psychology 2017;8:557.##23. Dupuy M, Misdrahi D, N’Kaoua B, Tessier A, Bouvard A, Schweitzer P, et al. Mobile cognitive testing in patients with schizophrenia: A controlled study of feasibility and validity. Journal de Thérapie Comportementale et Cognitive 2018;28(4):204-13. https://doi.org/10.1016/j.jtcc.2018.02.002##24. Kimhy D, Myin-Germeys I, Palmier-Claus J, Swendsen J. Mobile assessment guide for research in schizophrenia and severe mental disorders. Schizophr Bull 2012;38(3):386-95.##25. Granholm E, Loh C, Swendsen J. Feasibility and validity of computerized ecological momentary assessment in schizophrenia. Schizophr Bull 2008;34(3):507-14.##26. 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بررسی کیفیت برنامه‌های موبایل فارسی مربوط به بیماران مبتلا به بیماری‌های مزمن Investigating the Quality of Persian Mobile Applications Related to Patients with Chronic Diseases 1 1 مقدمه: امروزه استفاده از برنامه‌های کاربردی تلفن ‌همراه‌ برای کمک به خودمراقبتی در بیماران مبتلا به بیماری‌های مزمن افزایش یافته است. هدف از انجام این مطالعه بررسی کیفیت برنامه‌های موبایل فارسی مربوط به بیماران مبتلا به دیابت و فشارخون بود. روش: این مطالعه تحلیلی در زمستان 1397 بر روی کلیه اپلیکیشن‌های فارسی تلفن همراه مربوط به دیابت و فشارخون انجام شد. در این مطالعه برنامه‌های موبایل، با استفاده از کلیدواژه‌های دیابت، قندخون و فشار خون، در فروشگاه کافه­بازار جستجو شدند. در این مطالعه از سیستم امتیازدهی به برنامه‌ها (APPLICATIONS) استفاده شد. ارزیابی این برنامه‌ها توسط دو ارزیاب انجام و در موارد عدم توافق، از ارزیاب سوم کمک گرفته ‌شد. نتایج: به طورکلی 179 برنامه کاربردی مورد بررسی قرار گرفت. به ترتیب میانگین و انحراف معیار سطح کیفیت برنامه‌های مربوط به دیابت و فشارخون براساس ابزار استفاده‌ شده، 1/49±7/31 و 1/24± 7/22 از نمره 16 بود. بیش از 75% این برنامه‌ها دارای جامعیت بوده و در کمتر از ۷% این برنامه‌ها، از منابع معتبر علمی استفاده شده بود. استفاده از بیش‌ از 80% این برنامه‌ها آسان و بدون نیاز اینترنت بود. نتیجه‌گیری: نتایج نشان ‌داد اغلب برنامه‌های فارسی تلفن همراه مربوط به دیابت و فشارخون در سطح متوسطی قرار دارند. اغلب طراحان برنامه‌های موبایل، به جنبه‌های تعاملی برنامه‌ها توجه داشته‌اند و از ارائه محتوای علمی غافل شده‌اند. به مدیران فروشگاه‌های برنامه تلفن همراه پیشنهاد می‌شود، پیش از قرار دادن برنامه‌های حوزه سلامت در دسترس عموم، آن‌ها را مورد ارزیابی قرار ‌داده تا برنامه‌های با کیفیت پایین در دسترس افراد قرار نگیرد. 2 Introduction: Today, the use of mobile applications to help self-care in patients with chronic diseases has increased. The objective of this study was to investigate the quality of Persian mobile applications related to patients with diabetes and hypertension. Method: This analytical study was conducted on all Persian mobile applications related to diabetes and hypertension in 2019. The mobile applications were searched in the Café Bazaar app store using the keywords such as “diabetes”, “blood sugar” and “blood pressure”. The APPLICATIONs Scoring System was used in this study. Two evaluators assessed the applications and in cases of disagreement a third evaluator was consulted. Results: In total, 179 Persian mobile applications were investigated. The mean and standard deviation of the quality of applications related to diabetes and hypertension based on the applied tools were 7.31±1.49 and 7.22±1.24 out of 16, respectively. More than 75% of these applications were comprehensive and less than 7% used authentic scientific resources. More than 80% of these applications were user-friendly and could work offline. Conclusion: The results of this study showed that most of the Persian mobile applications related to diabetes and hypertension were at a moderate level. Most of the mobile application developers took into account the interactive aspects of applications but paid less attention to providing a scientific content. Evaluating the quality of health-related mobile applications is recommended to app store managers before low-quality applications are available to public. 273 281 2020/10/312019/10/82019/07/142019/10/292019/12/5 1398/9/14 2020/11/32020/01/82020/02/32020/04/182020/05/11 1399/2/22 رضا عباسی Reza Abbasi دانشجوی دکتری تخصصی مدیریت اطلاعات سلامت، مرکز تحقیقات مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی کاشان، کاشان، ایران ایران rezaabbasi2001@gmail.com احسان نبوتی Ehsan Nabovati استادیار انفورماتیک پزشکی، گروه مدیریت و فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی کاشان، کاشان، ایران ایران enabovati@gmail.com احمد رئیسی Ahmad Raeesi دانشجوی دکتری تخصصی انفورماتیک پزشکی، گروه انفورماتیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران ایران ah.raesse@gmail.com فائزه استادمحمدی Faezeh Ostadmohammadi دانشجوی کارشناسی فن‌آوری اطلاعات سلامت، کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی کاشان، کاشان، ایران ایران f.ostad76@gmail.com Mobile Health Quality Mobile Applications Diabetes Hypertension سلامت همراه کیفیت برنامه های موبایل دیابت فشارخون 1. Shrestha A, Singh SB, Khanal VK, Bhattarai S, Maskey R, Pokharel PK. Health Literacy and Knowledge of Chronic Diseases in Nepal. HLRP: Health Lit Res Pract 2018;2(4):e221-e30. doi: 10.3928/24748307-20181025-01##2. Karimi S, Javadi M, Jafarzadeh F. Economic burden and costs of chronic diseases in Iran and the world. Health Information Management 2012;8(7):984-96. [In Persian]##3. World Health Organization (WHO). Chronic diseases and health promotion: Integrated chronic disease prevention and control. [cited 2019 Oct 15]. Available from: https://www.who.int/chp/about/integrated_cd/en/. ##4. Kitsiou S, Pare G, Jaana M, Gerber B. Effectiveness of mHealth interventions for patients with diabetes: an overview of systematic reviews. PloS one 2017;12(3):e0173160. doi: 10.1371/journal.pone.0173160##5. Cho N, Shaw J, Karuranga S, Huang Y, da Rocha Fernandes J, Ohlrogge A, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-81. doi: 10.1016/j.diabres.2018.02.023##6. Kaplan MS, Huguet N, Feeny DH, McFarland BH. Self-reported hypertension prevalence and income among older adults in Canada and the United States. Soc Sci Med 2010;70(6):844-9. doi: 10.1016/j.socscimed.2009.11.019##7. Samiei Siboni F, Alimoradi Z, Sadegi T. Impact of corrective life style educational program on controlling stress, anxiety ,and depression in hypertensives. J Birjand Univ Med Sci 2013;19(6):1-9. [In Persian]##8. Global Health Observatory (GHO) data: Raised blood pressure [cited 2019 Oct 15]. Available from: https://www.who.int/gho/ncd/risk_factors/blood_pressure_prevalence_text/en/.##9. Didem A, Unal A, Alaettin U, Mustafa T. Prevalence of hypertension among individuals aged 50 years and over and its impact on health related quality of life in a semi-rural area of western Turkey. Chin Med J (Engl) 2008;121(16):1524-31.##10. Piette JD, Valverde H, Marinec N, Jantz R, Kamis K, Lazo de la Vega C, et al. Establishing an independent mobile health programme for chronic disease self-management support in Bolivia. Front Public Health 2014; 2: 95. doi: 10.3389/fpubh.2014.00095##11. Miri J, Naderipour A, Ashtarian H, Hashemian A. The Effect of Chronic Disease Self-Management Program on Health Status of Patients Undergoing Coronary Artery Bypass Graft Surgery. Crit Care Nurs J 2016; 9(1):e5103.##12. Dou K, Yu P, Deng N, Liu F, Guan Y, Li Z, et al. Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR Mhealth Uhealth 2017;5(12):e177. doi: 10.2196/mhealth.7886##13. Tan MY, Magarey J. Self-care practices of Malaysian adults with diabetes and sub-optimal glycaemic control. Patient Educ Couns 2008;72(2):252-67. doi: 10.1016/j.pec.2008.03.017##14. Hooshmandja M, Aliabadi K, Nili M, Mohammadi A, Delavari A. Designing and Validation of Mobile-based Instruction Model For Diabetes Self-care. Edu Strategy Med Sci 2017;10(4):312-21. [In Persian]##15. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015;17(2):e52. doi: 10.2196/jmir.3951##16. Masterson Creber RM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth 2016;4(2):e74. doi: 10.2196/mhealth.5882##17. Powell AC, Landman AB, Bates DW. In Search of a Few Good Apps. JAMA 2014;311(18):1851-2. doi: 10.1001/jama.2014.2564##18. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015; 3(1): e27. doi: 10.2196/mhealth.3422##19. Stoyanov SR, Hides L, Kavanagh DJ, Wilson H. Development and validation of the user version of the Mobile Application Rating Scale (uMARS). JMIR Mhealth Uhealth 2016;4(2):e72. doi: 10.2196/mhealth.5849##20. Domnich A, Arata L, Amicizia D, Signori A, Patrick B, Stoyanov S, et al. Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention. BMC Med Inform Decis Mak 2016;16:83. doi: 10.1186/s12911-016-0323-2##21. Haffey F, Brady RR, Maxwell S. A comparison of the reliability of smartphone apps for opioid conversion. Drug Saf 2013;36(2):111-7. doi: 10.1007/s40264-013-0015-0##22. Dubey D, Amritphale A, Sawhney A, Amritphale N, Dubey P, Pandey A. Smart phone applications as a source of information on stroke. J Stroke 2014;16(2):86-90. doi: 10.5853/jos.2014.16.2.86##23. Ghazi-Saeedi M, Rostamniakan-Kalhori S, Yasini M, Nouri R. Mobile Health Apps: References Reliability Analysis. Health Inf Manag 2017;14(1):44-8. [In Persian]##24. An Digital. Comparison of Iranian android markets. [cited 2019 Apr 15]. Available from: http://ondigital.ir/Article/126/. ##25. It Resan. The papularity of the bazar app store among iranian users [cited 2019 Apr 15]. Available from: https://itresan.com/88320/. ##26. Ayyaswami V, Padmanabhan DL, Crihalmeanu T, Thelmo F, Prabhu AV, Magnani JW. Mobile health applications for atrial fibrillation: A readability and quality assessment. Int J Cardiol 2019;293:288-93. doi: 10.1016/j.ijcard.2019.07.026##27. Shen N, Levitan M-J, Johnson A, Bender JL, Hamilton-Page M, Jadad AAR, et al. Finding a depression app: a review and content analysis of the depression app marketplace. JMIR mHealth uHealth 2015;3(1):e16. doi:10.2196/mhealth.3713##28. Pandey A, Hasan S, Dubey D, Sarangi S. Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J Cancer Edu 2013;28(1):138-42. doi: 10.1007/s13187-012-0446-9##29. Chavez S, Fedele D, Guo Y, Bernier A, Smith M, Warnick J, et al. Mobile apps for the management of diabetes. Diabetes Care 2017;40(10):e145-e6. doi: 10.2337/dc17-0853##30. Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar disorder: a systematic review of features and content quality. J Med Internet Res 2015;17(8):e198. doi: 10.2196/jmir.4581##31. Tinschert P, Jakob R, Barata F, Kramer JN, Kowatsch T. The potential of mobile apps for improving asthma self-management: a review of publicly available and well-adopted asthma apps. JMIR Mhealth Uhealth 2017;5(8):e113. doi: 10.2196/mhealth.7177##32. Richardson B, Dol J, Rutledge K, Monaghan J, Orovec A, Howie K, et al. Evaluation of Mobile Apps Targeted to Parents of Infants in the Neonatal Intensive Care Unit: Systematic App Review. JMIR mHealth uHealth 2019;7(4):e11620. doi: 10.2196/11620##1. Shrestha A, Singh SB, Khanal VK, Bhattarai S, Maskey R, Pokharel PK. Health Literacy and Knowledge of Chronic Diseases in Nepal. HLRP: Health Lit Res Pract 2018;2(4):e221-e30. doi: 10.3928/24748307-20181025-01##2. Karimi S, Javadi M, Jafarzadeh F. Economic burden and costs of chronic diseases in Iran and the world. Health Information Management 2012;8(7):984-96. [In Persian]##3. World Health Organization (WHO). Chronic diseases and health promotion: Integrated chronic disease prevention and control. [cited 2019 Oct 15]. Available from: https://www.who.int/chp/about/integrated_cd/en/. ##4. Kitsiou S, Pare G, Jaana M, Gerber B. Effectiveness of mHealth interventions for patients with diabetes: an overview of systematic reviews. PloS one 2017;12(3):e0173160. doi: 10.1371/journal.pone.0173160##5. Cho N, Shaw J, Karuranga S, Huang Y, da Rocha Fernandes J, Ohlrogge A, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-81. doi: 10.1016/j.diabres.2018.02.023##6. Kaplan MS, Huguet N, Feeny DH, McFarland BH. Self-reported hypertension prevalence and income among older adults in Canada and the United States. Soc Sci Med 2010;70(6):844-9. doi: 10.1016/j.socscimed.2009.11.019##7. Samiei Siboni F, Alimoradi Z, Sadegi T. Impact of corrective life style educational program on controlling stress, anxiety ,and depression in hypertensives. J Birjand Univ Med Sci 2013;19(6):1-9. [In Persian]##8. Global Health Observatory (GHO) data: Raised blood pressure [cited 2019 Oct 15]. Available from: https://www.who.int/gho/ncd/risk_factors/blood_pressure_prevalence_text/en/.##9. Didem A, Unal A, Alaettin U, Mustafa T. Prevalence of hypertension among individuals aged 50 years and over and its impact on health related quality of life in a semi-rural area of western Turkey. Chin Med J (Engl) 2008;121(16):1524-31.##10. Piette JD, Valverde H, Marinec N, Jantz R, Kamis K, Lazo de la Vega C, et al. Establishing an independent mobile health programme for chronic disease self-management support in Bolivia. Front Public Health 2014; 2: 95. doi: 10.3389/fpubh.2014.00095##11. Miri J, Naderipour A, Ashtarian H, Hashemian A. The Effect of Chronic Disease Self-Management Program on Health Status of Patients Undergoing Coronary Artery Bypass Graft Surgery. Crit Care Nurs J 2016; 9(1):e5103.##12. Dou K, Yu P, Deng N, Liu F, Guan Y, Li Z, et al. Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR Mhealth Uhealth 2017;5(12):e177. doi: 10.2196/mhealth.7886##13. Tan MY, Magarey J. Self-care practices of Malaysian adults with diabetes and sub-optimal glycaemic control. Patient Educ Couns 2008;72(2):252-67. doi: 10.1016/j.pec.2008.03.017##14. Hooshmandja M, Aliabadi K, Nili M, Mohammadi A, Delavari A. Designing and Validation of Mobile-based Instruction Model For Diabetes Self-care. Edu Strategy Med Sci 2017;10(4):312-21. [In Persian]##15. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015;17(2):e52. doi: 10.2196/jmir.3951##16. Masterson Creber RM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth 2016;4(2):e74. doi: 10.2196/mhealth.5882##17. Powell AC, Landman AB, Bates DW. In Search of a Few Good Apps. JAMA 2014;311(18):1851-2. doi: 10.1001/jama.2014.2564##18. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015; 3(1): e27. doi: 10.2196/mhealth.3422##19. Stoyanov SR, Hides L, Kavanagh DJ, Wilson H. Development and validation of the user version of the Mobile Application Rating Scale (uMARS). JMIR Mhealth Uhealth 2016;4(2):e72. doi: 10.2196/mhealth.5849##20. Domnich A, Arata L, Amicizia D, Signori A, Patrick B, Stoyanov S, et al. Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention. BMC Med Inform Decis Mak 2016;16:83. doi: 10.1186/s12911-016-0323-2##21. Haffey F, Brady RR, Maxwell S. A comparison of the reliability of smartphone apps for opioid conversion. Drug Saf 2013;36(2):111-7. doi: 10.1007/s40264-013-0015-0##22. Dubey D, Amritphale A, Sawhney A, Amritphale N, Dubey P, Pandey A. Smart phone applications as a source of information on stroke. J Stroke 2014;16(2):86-90. doi: 10.5853/jos.2014.16.2.86##23. Ghazi-Saeedi M, Rostamniakan-Kalhori S, Yasini M, Nouri R. Mobile Health Apps: References Reliability Analysis. Health Inf Manag 2017;14(1):44-8. [In Persian]##24. An Digital. Comparison of Iranian android markets. [cited 2019 Apr 15]. Available from: http://ondigital.ir/Article/126/. ##25. It Resan. The papularity of the bazar app store among iranian users [cited 2019 Apr 15]. Available from: https://itresan.com/88320/. ##26. Ayyaswami V, Padmanabhan DL, Crihalmeanu T, Thelmo F, Prabhu AV, Magnani JW. Mobile health applications for atrial fibrillation: A readability and quality assessment. Int J Cardiol 2019;293:288-93. doi: 10.1016/j.ijcard.2019.07.026##27. Shen N, Levitan M-J, Johnson A, Bender JL, Hamilton-Page M, Jadad AAR, et al. Finding a depression app: a review and content analysis of the depression app marketplace. JMIR mHealth uHealth 2015;3(1):e16. doi:10.2196/mhealth.3713##28. Pandey A, Hasan S, Dubey D, Sarangi S. Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J Cancer Edu 2013;28(1):138-42. doi: 10.1007/s13187-012-0446-9##29. Chavez S, Fedele D, Guo Y, Bernier A, Smith M, Warnick J, et al. Mobile apps for the management of diabetes. Diabetes Care 2017;40(10):e145-e6. doi: 10.2337/dc17-0853##30. Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar disorder: a systematic review of features and content quality. J Med Internet Res 2015;17(8):e198. doi: 10.2196/jmir.4581##31. Tinschert P, Jakob R, Barata F, Kramer JN, Kowatsch T. The potential of mobile apps for improving asthma self-management: a review of publicly available and well-adopted asthma apps. JMIR Mhealth Uhealth 2017;5(8):e113. doi: 10.2196/mhealth.7177##32. Richardson B, Dol J, Rutledge K, Monaghan J, Orovec A, Howie K, et al. Evaluation of Mobile Apps Targeted to Parents of Infants in the Neonatal Intensive Care Unit: Systematic App Review. JMIR mHealth uHealth 2019;7(4):e11620. doi: 10.2196/11620 ##

بررسی عوامل مؤثر بر پذیرش دستگاه‌های هوشمند مراقبت‌های بهداشتی با استفاده از مدل پذیرش فناوری ترکیبی An Exploration of Factors Affecting the Acceptance of Smart Healthcare Devices Using Integrated Technology Acceptance Model 1 1 مقدمه: امروزه اینترنت اشیاء برای بیماران و مراقبین سلامت انتقال فرایند درمان بیماران را فراهم کرده و بیماران را قادر به مدیریت و دریافت کمک از گروه مراقبین سلامت و ابزارهای سیار ساخته است. این موضوع به عنوان یک راه‌حل امیدوار کننده به منظور ارتقاء کیفیت مراقبت بهداشتی مورد توجه قرار گرفته است؛ هدف از مطالعه حاضر بررسی عوامل مؤثر بر پذیرش دستگاه‌های هوشمند مراقبت‌های بهداشتی با استفاده از مدل پذیرش فناوری ترکیبی می باشد. روش: در این مطالعه توصیفی –پیمایشی، 169 نفر از بین 300 نفر بیمار بیمارستان گیل واقع در شهر رشت، براساس جدول مورگان برای نمونه انتخاب گردیدند. جهت بررسی فرضیه از پرسشنامه Papa و همکاران و Li و همکاران استفاده شد. برای تحلیل داده ها تکنیک معادلات ساختاری و آزمون t-test به کار گرفته شد. نتایج: طبق یافته‌های پژوهش تأثیر«نفوذ‌پذیری»، «راحتی»، «ریسک اجتماعی ادراک‌ شده»، «ریسک عملکرد» بر «سودمندی و سهولت ادراک ‌شده»، تأثیر «سودمندی و سهولت ادراک‌ شده» بر «نگرش نسبت به پذیرش دستگاه‌های هوشمند مراقبت بهداشتی» و در نهایت تأثیر «نگرش نسبت به پذیرش دستگاه‌های هوشمند مراقبت بهداشتی» بر «قصد رفتاری استفاده از این دستگاه‌ها» تأیید شد. نتیجه‌گیری: نتایج نشان داد که مدل فناوری ترکیبی در شناسایی و بررسی عوامل مؤثر بر پذیرش دستگاه‌های هوشمند مراقبت بهداشتی می‌تواند نقش مؤثری داشته باشد و به عنوان یک منبع اطلاعاتی مهم جهت استفاده از این دستگاه‌ها مورد استفاده قرار گیرد. 2 Introduction: Recently, the Internet of Things (IoT) allows patients and healthcare providers to transfer the treatment process to the patients and enables them to manage the disease and receive help from the healthcare team and mobile devices. This has been considered as a promising solution to improve the quality of healthcare. The objective of this study was to investigate the factors affecting the acceptance of smart healthcare devices using an integrated technology acceptance model. Method: In this descriptive-survey research, 169 out of 300 patients of Gil hospital in Rasht were selected based on Morgan table. The questionnaires by Papa et al. and Li et al. were applied to test the hypothesis. The structural equation technique and t-test were used to analyze the data. Results: According to the research findings, the effect of “permeability”, “comfort”, “perceived social risk”, and “performance risk” on “perceived usefulness and ease”, the effect of “perceived usefulness and ease” on “attitude towards the acceptance of smart healthcare devices”, and finally, the effect of “attitudes toward the acceptance of smart healthcare devices” on the “behavioral intention to use these devices” were confirmed. Conclusion: The results showed that the integrated technology model can play an effective role in identifying and examining the factors affecting the acceptance of smart healthcare devices and can be utilized as an important source of information for the use of these devices. 282 292 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/23 1398/8/1 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/20 1399/4/30 آتنا گلشن Atena Golshan کارشناسی ارشد مدیریت فناوری اطلاعات، گروه مدیریت فناوری اطلاعات، دانشگاه آزاد اسلامی لاهیجان، ایران ایران h.mosafer2008@gmail.com مریم پورنصیر رودبنه Maryam Pournasir Roudbane دکترای مدیریت فناوری اطلاعات، استادیار، گروه مدیریت فناوری اطلاعات، دانشگاه آزاد اسلامی لاهیجان، ایران ایران pournasir@liau.ac.ir Internet of Things Smart Healthcare Devices Perceived Usefulness Perceived Ease Integrated Technology Acceptance Model اینترنت اشیاء دستگاه‌های هوشمند مراقبت‌های بهداشتی سودمندی ادارک شده سهولت ادراک شده مدل پذیرش فناوری ترکیبی 1. Khedmatgozar HR. The role of internet of things (iot) in knowledge management systems (case study: performance management of Yazd municipality staff). Journal of Information Technology Management 2015; 7(3): 553-72. [In Persian]##2. Hashemi Rad S. Smart Health; 2020 [cited 2020 Apr 9]. Available from: https://www.iotfig.com##3. Asadi M, Chaleh Chaleh A. An Overview of Internet-Based Health Care Methods. National Conference on New Technologies in Electrical and Computer Engineering; 2018 Jan 16; Esfahan: Daneshgahi Jihad; 2017. [In Persian]##4. Papa A, Mital M, Pisano P, Del Giudice M. E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change 2020;153:119226. https://doi.org/10.1016/j.techfore.2018.02.018##5. Li J, Ma Q, Chan AH, Man SS. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 201975:162-9. doi: 10.1016/j.apergo.2018.10.006##6. Momeni M, Faal Ghayumi A. Statistical Analysis with SPSS. 11th ed. Tehran: Moallef; 2017. [In Persian].##7. Davari A, Reza zade A. Stractural Equation with PLS Software. 4th ed. Tehran: Daneshgahi Jihad; 2017. [In Persian]##8. Chin WW. The partial least squares approach to structural equation modeling. Modern Methods For Business Research 1998;295(2):295-336.##9. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Advances in International Marketing 2009; 20: 277-319. doi: 10.1108/S1474-7979(2009)0000020014##10. Tenenhaus M, Amato Sو Vinzi VE. A Global Goodness-of-Fit Index for PLS Structural Equation Modelling. Proceedings of the XLII SIS Scientific Meeting 2004; 1: 739-42.##11. Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly 2009; 33(1): 177-95. doi: 10.2307/20650284##12. Mahmoudi Meymand M, Shayan A, Hosseini S M. Effective factors on the adoption of mobile health technology: a case study on pregnant women's application. Journal of Health and Biomedical Informatics 2018; 5(3) :373-83. [In Persian]##13. Mehdipour Y, Khammarnia M, Karimi A, Alipour J, Seyedzaei F, Ebrahimi S. Effective factors in acceptance of mobile health from physicians point of view. J Mod Med Info Sci 2017;3(1): 29-36. [In Persian]##1. Khedmatgozar HR. The role of internet of things (iot) in knowledge management systems (case study: performance management of Yazd municipality staff). Journal of Information Technology Management 2015; 7(3): 553-72. [In Persian]##2. Hashemi Rad S. Smart Health; 2020 [cited 2020 Apr 9]. Available from: https://www.iotfig.com##3. Asadi M, Chaleh Chaleh A. An Overview of Internet-Based Health Care Methods. National Conference on New Technologies in Electrical and Computer Engineering; 2018 Jan 16; Esfahan: Daneshgahi Jihad; 2017. [In Persian]##4. Papa A, Mital M, Pisano P, Del Giudice M. E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change 2020;153:119226. https://doi.org/10.1016/j.techfore.2018.02.018##5. Li J, Ma Q, Chan AH, Man SS. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 201975:162-9. doi: 10.1016/j.apergo.2018.10.006##6. Momeni M, Faal Ghayumi A. Statistical Analysis with SPSS. 11th ed. Tehran: Moallef; 2017. [In Persian].##7. Davari A, Reza zade A. Stractural Equation with PLS Software. 4th ed. Tehran: Daneshgahi Jihad; 2017. [In Persian]##8. Chin WW. The partial least squares approach to structural equation modeling. Modern Methods For Business Research 1998;295(2):295-336.##9. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Advances in International Marketing 2009; 20: 277-319. doi: 10.1108/S1474-7979(2009)0000020014##10. Tenenhaus M, Amato Sو Vinzi VE. A Global Goodness-of-Fit Index for PLS Structural Equation Modelling. Proceedings of the XLII SIS Scientific Meeting 2004; 1: 739-42.##11. Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly 2009; 33(1): 177-95. doi: 10.2307/20650284##12. Mahmoudi Meymand M, Shayan A, Hosseini S M. Effective factors on the adoption of mobile health technology: a case study on pregnant women's application. Journal of Health and Biomedical Informatics 2018; 5(3) :373-83. [In Persian]##13. Mehdipour Y, Khammarnia M, Karimi A, Alipour J, Seyedzaei F, Ebrahimi S. Effective factors in acceptance of mobile health from physicians point of view. J Mod Med Info Sci 2017;3(1): 29-36. [In Persian] ##

بررسی تأثیر به‌کارگیری محیط واقعیت مجازی و تابلو کنترل هوشمند بر توانبخشی بالاتنه Investigating the Effect of Virtual Reality Environment and Intelligent Control Panel on the Rehabilitation of Upper Limb 1 1 مقدمه: کاردرمانی و انجام تمرینات حرکتی خاص یکی از فرآیندهای بهبود در افراد آسیب‌ دیده است که پس از تجویز پزشک، بایستی بیماران نیازمند این روش درمانی را دنبال نمایند. هدف از این پژوهش ارزیابی تأثیر استفاده از محیط واقعیت مجازی (Virtual reality) و تعامل با تجهیزات سخت‌افزاری طراحی شده بر درمان و توانبخشی بیماران دچار آسیب در ناحیه بالاتنه می‌باشد. روش: در این پژوهش، فرآیند طراحی و ساخت شامل دو بخش نرم‌افزار و سخت‌افزار است. در بخش نرم‌افزار به منظور دسترسی به محیط واقعیت مجازی تحت سیستم عامل ویندوز محیط واقعیت مجازی طراحی و کدهای لازم نوشته شد و سپس ارتباط با بخش سخت‌افزار و میکروکنترلر موجود در برد آردوینو (Arduino) فراهم گردید تا بتوان فرامین صادر شده از جعبه فرمان را به محیط واقعیت مجازی صادر کرد و متناسب با اندازه حرکت موس، حرکتی در محیط مجازی داشت. نتایج: با توجه به ماهیت پژوهش، به‌منظور ارزیابی نرم‌افزار از معیار مارتیلا و جیمز استفاده شد. هم‌چنین به‌منظور اندازه‌گیری تأثیر این روش در کاهش سطح درد بیمار از مقیاس عددی نرخ‌بندی درد (Numeric Pain Rating Scale) استفاده گردید و عملکرد محیط واقعیت مجازی در کاهش سطح درد مورد ارزیابی قرار گرفت. نتیجه‌گیری: نتایج نشان می‌دهد که استفاده از واقعیت مجازی در توانبخشی، با توجه به نوع گرافیک ارائه شده و سخت‌افزار طراحی شده، علاوه بر این‌که سبب کاهش زمان درمان و سطح درد می‌شود، به ترغیب بیمار در استفاده از نرم‌افزار و ادامه‌ فعالیت در مکانی غیر از کلینیک نیز می‌انجامد. 2 Introduction: Occupational therapy and performing specific motor activities are among the healing processes for injured people that should be followed by patients in need after the doctor’s prescription. The objective of this study was to evaluate the effect of using virtual reality environments and interacting with hardware designed for the treatment and rehabilitation of patients with upper limb injuries. Method: In this study, the design and manufacturing process consisted of two parts: software and hardware. In the software section, in order to access the virtual reality environment under the Windows operating system, the virtual reality environment was designed and coded and then the connection to the hardware and microcontroller section of the Arduino board was provided so that commands could be exported from the controller to the virtual reality environment and depending on the mouse motion, a movement in the virtual environment would be made. Results: Given the nature of this research, the criteria proposed by Martilla and James were used to evaluate the software. Moreover, Numeric Pain Rating Scale (NRS) was used to measure the effect of this method on reducing pain and the performance of virtual reality environment in reducing pain was evaluated. Conclusion: The results showed that using virtual reality for rehabilitation, with accordance to the type of graphics provided and the hardware designed, in addition to reducing treatment time and pain level, encourages the patients to use the software and continue the activities in a place other than the clinic. 293 303 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/9 1398/8/18 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/11 1398/10/21 مجید اسلامی Majid Eslami دانشجوی دکتری تخصصی مکانیک، گروه مکانیک، واحد خمینی‌شهر، دانشگاه آزاد اسلامی، خمینی‌شهر، اصفهان، ایران ایران Majid.eslami@iaukhsh.ac.ir مصطفی پیرمرادیان Mostafa Pirmoradian دکتری تخصصی مهندسی مکانیک، استادیار‌، گروه مکانیک، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی‌شهر، اصفهان، ایران ایران pirmoradian@iaukhsh.ac.ir علی مختاریان Ali Mokhtarian دکتری تخصصی مهندسی مکانیک، استادیار‌، گروه مکانیک، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی‌شهر، اصفهان، ایران ایران mokhtarian@iaukhsh.ac.ir سیدعلیرضا سیف زاده Seyed AliReza Seifzadeh دکتری تخصصی مهندسی مکانیک (بیومکانیک)، استادیار‌، گروه مهندسی پزشکی، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی‌شهر، اصفهان، ایران ایران seifzadeh@iaukhsh.ac.ir سیدمسعود رفیعائی Seyed Masoud Rafiaei دکتری تخصصی ارتز و پروتز، استادیار‌، گروه اعضاء مصنوعی و وسایل‌کمکی، دانشگاه علوم پزشکی، اصفهان، ایران ایران M_rafiaei@rehab.mui.ac.ir Virtual Reality Rehabilitation Occupational Therapy Arduino Computer Graphics واقعیت مجازی توانبخشی کاردرمانی آردوینو گرافیک رایانه‌ای 1. Teasell R, Meyer MJ, McClure A, Pan C, Murie-Fernandez M, Foley N, Salter K. Stroke rehabilitation: an international perspective. Top Stroke Rehabil 2009;16(1):44-56. doi: 10.1310/tsr1601-44##2. Eslami M, Mokhtarian A, Pirmoradian M, Seifzadeh A, Rafiaei M. Design and fabrication of a passive upper limb rehabilitation robot with adjustable automatic balance based on variable mass of end-effector. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2020;42(12):1-8. ##3. Eslami M, Mokhtarian A, Pirmoradian M, Seifzadeh SA, Rafiaei S M. Designing and Creating a Virtual Reality Environment and a Wearable Glove with Control and Evaluation Capability to Rehabilitate Patients. Journal of Health and Biomedical Informatics 2020; 7(2) :161-70. [In Persian]##2. Mandal S. Brief introduction of virtual reality & its challenges. International Journal of Scientific & Engineering Research 2013;4(4):304-9.##5. Mazuryk T, Gervautz M. History, Applications, Technology and Future. Virtual Reality; 1996.####6. Horváthová D, Siládi V, Lacková E. Phobia treatment with the help of virtual reality. 13th International Scientific Conference on Informatics; 2015 Nov 18-20;. Poprad, Slovakia: IEEE; 2015. p. 114-19. doi: 10.1109/Informatics.2015.7377818##7. Bellani M, Fornasari L, Chittaro L, Brambilla P. Virtual reality in autism: state of the art. Epidemiol Psychiatr Sci 2011;20(3):235-8. doi: 10.1017/s2045796011000448##8. Lucca LF. Virtual reality and motor rehabilitation of the upper limb after stroke: a generation of progress?. J Rehabil Med 2009;41(12):1003-100. doi: 10.2340/16501977-0405##9. Rose T, Nam CS, Chen KB. Immersion of virtual reality for rehabilitation-Review. Appl Ergon 2018;69:153-61. doi: 10.1016/j.apergo.2018.01.009##10. Boian R, Sharma A, Han C, Merians A, Burdea G, Adamovich S, et al. Virtual reality-based post-stroke hand rehabilitation. Stud Health Technol Inform 2002;85:64-70. ##11. Nef T, Mihelj M, Kiefer G, Perndl C, Muller R, Riener R. ARMin-Exoskeleton for arm therapy in stroke patients. 10th International Conference on Rehabilitation Robotics; 2007 Jun 13-15; Noordwijk, Netherlands: IEEE; 2007. p. 68-74. doi: 10.1109/ICORR.2007.4428408##12. Lo CL, Tseng HT. Predicting rehabilitation treatment helpfulness to stroke patients: A supervised learning approach. Artificial Intelligence Research 2017;6(2):1-9. doi: 10.5430/air.v6n2p1##13. Krabben T, Prange GB, Molier BI, Stienen AH, Jannink MJ, Buurke JH, Rietman JS. Influence of gravity compensation training on synergistic movement patterns of the upper extremity after stroke, a pilot study. J Neuroeng Rehabil 2012;9:44. doi: 10.1186/1743-0003-9-44##14. Herianto WY, Saryanto WY, Cahyadi AI. Modeling and design of low cost lower limb rehabilitation robot control system for post-stroke patient using PWM controller. International Journal of Mechanical & Mechatronics Engineering 2016; 16(1): 101–8.##15. Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The stroke impact scale version 2.0: evaluation of reliability, validity, and sensitivity to change. Stroke 1999;30(10):2131-40. doi: 10.1161/01.str.30.10.2131##16. Wolf SL, Lecraw DE, Barton LA, Jann BB. Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients. Exp Neurol 1989;104(2):125-32. doi: 10.1016/s0014-4886(89)80005-6##17. Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, Ugurbil K. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 2002;125(4):773-88. doi: 10.1093/brain/awf091##18. Silva FH, Fernandes PO. Importance-performance analysis as a tool in evaluating higher education service quality: the empirical results of ESTiG (IPB). In the 17th International Business Information Management Association Conference; 2011. p. 306-15.##1. Teasell R, Meyer MJ, McClure A, Pan C, Murie-Fernandez M, Foley N, Salter K. Stroke rehabilitation: an international perspective. Top Stroke Rehabil 2009;16(1):44-56. doi: 10.1310/tsr1601-44##2. Eslami M, Mokhtarian A, Pirmoradian M, Seifzadeh A, Rafiaei M. Design and fabrication of a passive upper limb rehabilitation robot with adjustable automatic balance based on variable mass of end-effector. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2020;42(12):1-8. ##3. Eslami M, Mokhtarian A, Pirmoradian M, Seifzadeh SA, Rafiaei S M. Designing and Creating a Virtual Reality Environment and a Wearable Glove with Control and Evaluation Capability to Rehabilitate Patients. Journal of Health and Biomedical Informatics 2020; 7(2) :161-70. [In Persian]##2. Mandal S. Brief introduction of virtual reality & its challenges. International Journal of Scientific & Engineering Research 2013;4(4):304-9.##5. Mazuryk T, Gervautz M. History, Applications, Technology and Future. Virtual Reality; 1996.####6. Horváthová D, Siládi V, Lacková E. Phobia treatment with the help of virtual reality. 13th International Scientific Conference on Informatics; 2015 Nov 18-20;. Poprad, Slovakia: IEEE; 2015. p. 114-19. doi: 10.1109/Informatics.2015.7377818##7. Bellani M, Fornasari L, Chittaro L, Brambilla P. Virtual reality in autism: state of the art. Epidemiol Psychiatr Sci 2011;20(3):235-8. doi: 10.1017/s2045796011000448##8. Lucca LF. Virtual reality and motor rehabilitation of the upper limb after stroke: a generation of progress?. J Rehabil Med 2009;41(12):1003-100. doi: 10.2340/16501977-0405##9. Rose T, Nam CS, Chen KB. Immersion of virtual reality for rehabilitation-Review. Appl Ergon 2018;69:153-61. doi: 10.1016/j.apergo.2018.01.009##10. Boian R, Sharma A, Han C, Merians A, Burdea G, Adamovich S, et al. Virtual reality-based post-stroke hand rehabilitation. Stud Health Technol Inform 2002;85:64-70. ##11. Nef T, Mihelj M, Kiefer G, Perndl C, Muller R, Riener R. ARMin-Exoskeleton for arm therapy in stroke patients. 10th International Conference on Rehabilitation Robotics; 2007 Jun 13-15; Noordwijk, Netherlands: IEEE; 2007. p. 68-74. doi: 10.1109/ICORR.2007.4428408##12. Lo CL, Tseng HT. Predicting rehabilitation treatment helpfulness to stroke patients: A supervised learning approach. Artificial Intelligence Research 2017;6(2):1-9. doi: 10.5430/air.v6n2p1##13. Krabben T, Prange GB, Molier BI, Stienen AH, Jannink MJ, Buurke JH, Rietman JS. Influence of gravity compensation training on synergistic movement patterns of the upper extremity after stroke, a pilot study. J Neuroeng Rehabil 2012;9:44. doi: 10.1186/1743-0003-9-44##14. Herianto WY, Saryanto WY, Cahyadi AI. Modeling and design of low cost lower limb rehabilitation robot control system for post-stroke patient using PWM controller. International Journal of Mechanical & Mechatronics Engineering 2016; 16(1): 101–8.##15. Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The stroke impact scale version 2.0: evaluation of reliability, validity, and sensitivity to change. Stroke 1999;30(10):2131-40. doi: 10.1161/01.str.30.10.2131##16. Wolf SL, Lecraw DE, Barton LA, Jann BB. Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients. Exp Neurol 1989;104(2):125-32. doi: 10.1016/s0014-4886(89)80005-6##17. Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, Ugurbil K. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 2002;125(4):773-88. doi: 10.1093/brain/awf091##18. Silva FH, Fernandes PO. Importance-performance analysis as a tool in evaluating higher education service quality: the empirical results of ESTiG (IPB). In the 17th International Business Information Management Association Conference; 2011. p. 306-15. ##

پیش‌بینی خطر ابتلاء به بیماری پوکی استخوان با استفاده از درخت تصمیم و شبکه عصبی Predicting the Risk of Osteoporosis Using Decision Tree and Neural Network 1 1 مقدمه: پوکی استخوان یکی از دلایل مهم ناتوانی و مرگ در افراد مسن است. هدف این تحقیق تعیین عوامل تأثیرگذار در بروز پوکی استخوان و ارائه مدل پیش‌بینی کننده‌ای جهت سرعت بخشیدن به تشخیص وکاهش هزینه‌ها می‌باشد. روش: در این مطالعه بنیادی توصیفی مدل جدیدی جهت شناسایی ویژگی‌های تأثیرگذار بر پوکی استخوان ارائه شده است. اطلاعات مربوط به 4083 نفر زن جهت کشف دانش با ابزار داده کاوی Clementine12 مورد بررسی قرار گرفته است. با استفاده از الگوریتم‌های داده‌کاوی شامل درخت تصمیم و شبکه عصبی مصنوعی قوانینی استخراج شده که می‌توان به عنوان الگویی برای پیش‌بینی وضعیت بیماران از آن‌ها استفاده کرد و در نهایت دقت مدل‌های ساخته شده با یکدیگر مقایسه شده‌اند. نتایج: این تحقیق مدل‌های متعدد را بر روی تعداد ویژگی‌های متفاوت بررسی و نتایج حاصل را به منظور یافتن بهترین مدل پیش‌بینی کننده از نظر دقت و صحت با هم مقایسه می­کند. دقت طبقه‌بندی مدل شبکه عصبی MLP با 92/14 درصد از دیگر الگوریتم‌های به کار رفته در این مطالعه بیشتر است. با توجه به شناسایی عوامل تأثیرگذار بر پوکی استخوان می‌توان برای یک نمونه جدید احتمال ابتلاء به این بیماری را پیش‌بینی کرد. نتیجه‌گیری: سازمان‌های متولی مراقبت‌های بهداشتی و سلامت همواره حجم زیادی از اطلاعات را جمع‌آوری می­کنند در حالی که این اطلاعات و داده‌ها به درستی مورد استفاده قرار نمی‌گیرند. این مطالعه نشان می‌دهد با کشف الگوها و روابط پنهان در این داده‌ها می‌توان از آن‌ها در جهت بهبود کیفیت ارائه خدمات تشخیصی و درمانی بهره برد. 2 Introduction: Osteoporosis is one of the major causes of disability and death in elderly people. The objective of this study was to determine the factors affecting the incidence of osteoporosis and provide a predictive model to accelerate diagnosis and reduce costs. Method: In this fundamental descriptive study, a new model was proposed to identify the factors affecting osteoporosis. Data related to 4083 women were investigated with Clementine12, the data mining tool, to discover knowledge. Using data mining algorithms, including decision tree and artificial neural network, some rules were extracted that can be used as a model to predict the condition of patients and finally, the accuracy of the proposed models were compared. Results: This study examined several models on a number of different characteristics and compared the results in terms of accuracy to find the best predictive model. The classification accuracy of the MLP neural network model was 92.14% which was higher than that of the other algorithms used in this study. According to the identification of factors affecting osteoporosis, the risk of developing this disease can be predicted for a new sample. Conclusion: Healthcare organizations are always gathering a lot of information while this data is not used properly. This study showed that the hidden patterns and relationships in this data can be discovered and used to improve the quality of diagnostic and treatment services. 304 317 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/92019/11/2 1398/8/11 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/112020/02/3 1398/11/14 امین عابدینی Amin Abedini کارشناسی ارشد هوش مصنوعی‌، کامپیوتر و فناوری اطلاعات‌، دانشگاه آزاد اسلامی قزوین، قزوین، ایران ایران aminabedini.ai@gmail.com عفت جبارپور Efat Jabarpour دانشجوی دکتری مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران ایران ejabarpoor@yahoo.com عباسعلی کشتکار Abbasali Keshtkar دانشیار، گروه توسعه آموزش علوم سلامت، دانشکده بهداشت، دانشگاه علوم پزشکی تهران، تهران، ایران ایران abkeshtkar@gmail.com Osteoporosis Data Mining Decision Tree Artificial Neural Network Clementine پوکی استخوان داده‌کاوی درخت تصمیم شبکه عصبی مصنوعی کلمنتاین 1. Eastell R. Treatment of postmenopausal osteoporosis. N Engl J Med 1998;338(11):736-46. doi: 10.1056/NEJM199803123381107https://www.jri.ir/documents/fullpaper/fa/170.pdf##2. World Health Organization (WHO). Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [‎‎‎‎meeting held in Rome from 22 to 25 June 1992]. Available from: https://apps.who.int/iris/bitstream/handle/10665/39142/WHO_TRS_843.pdf?sequence=1 World Health Organization. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [‎‎‎meeting held in Rome from 22 to 25 June 1992]‎‎‎. World Health Organization; 1994.##3. Seeman E, Hopper JL, Bach LA, Cooper ME, Parkinson E, McKay J, et al. Reduced bone mass in daughters of women with osteoporosis. N Engl J Med 1989;320(9):554-8. doi: 10.1056/NEJM198903023200903##4. Slemenda CW, Christian JC, Williams CJ, Norton JA, Johnston CC. Genetic determinants of bone mass in adult women: a reevaluation of the twin model and the potential importance of gene interaction on heritability estimates. J Bone Miner Res 1991;6(6):561-7. doi: 10.1002/jbmr.5650060606##5. Bonjour JP, Theintz G, Law F, Slosman D, Rizzoli R. Peak bone mass. Osteoporos Int 1994;4:7-13. doi: 10.1007/BF01623429##6. Saadi HF, Reed RL, Carter AO, Qazaq HS, Al Suhaili AR. Bone density estimates and risk factors for osteoporosis in young women. East Mediterr Health J 2001;7(4-5):730-7.##7. Ruiz JC, Mandel C, Garabedian M. Influence of spontaneous calcium intake and physical exercise on the vertebral and femoral bone mineral density of children and adolescents. J Bone Miner Res 1995;10(5):675-82. doi: 10.1002/jbmr.5650100502##8. Rizzoli R, Bonjour JP, Ferrari SL. Osteoporosis, genetics and hormones. J Mol Endocrinol 2001;26(2):79-94. doi: 10.1677/jme.0.0260079##9. Bass S, Pearce G, Bradney M, Hendrich E, Delmas PD, Harding A, Seeman E. Exercise before puberty may confer residual benefits in bone density in adulthood: studies in active prepubertal and retired female gymnasts. J Bone Miner Res 1998;13(3):500-7. doi: 10.1359/jbmr.1998.13.3.500.##10. Ardeshir Larijani MB, Mohajeri Tehrani MR., Soltani Z, Pazhouhi M. Mosteoporosis, Prevention, Diagnosis and Treatment. Journal of Reproduction and Infertility 2005; 6(1): 5-24. [In Persian]##11. Sambrook PN, Dequiker J, Rasp HH. Metabolic bone disease, Report of a WHO study group, Assessment of fracture risk and its application to screening for postmenopausal Osteoporosis. Geneva: WHO Technical Report Series; 1994.##12. Lakshmi KV, Padmavathamma M. Modeling an expert system for diagnosis of gestational diabetes mellitus based on risk factors. Journal of Computer Engineering 2013;8(3):29-32. ##13. Stafford GC, Kelley PE, Syka JE, Reynolds WE, Todd JF. Recent improvements in and analytical applications of advanced ion trap technology. International Journal of Mass Spectrometry and Ion Processes 1984;60(1):85-98. https://doi.org/10.1016/0168-1176(84)80077-4##14. Vijiyarani S, Sudha S. Disease prediction in data mining technique–a survey. International Journal of Computer Applications & Information Technology 2013;2(1):17-21. ##15. Moudani W, Shahin A, Chakik F, Rajab D. Intelligent predictive osteoporosis system. International Journal of Computer Applications 2011;32(5):28-37. ##16. Zhou XH, Li SL, Tian F, Cai BJ, Xie YM, Pei Y, et al. Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors. Stat Med 2012;31(7):643-52. doi: 10.1002/sim.4382##17. Sharifkhani M, Alizadeh S, Abbasi M, Ameri H. Providing a Model for Predicting the Risk of Osteoporosis Using Decision Tree Algorithms. J Mazandaran Univ Med Sci 2014; 24(116):110-18. [In Persain] ##18. Sathawane MK, Tuteja MR. Data Mining in Clinical Records to foretell the risk of Osteoporosis; 2015.##19. Nahar N, Ara F. Liver disease prediction by using different decision tree techniques. International Journal of Data Mining & Knowledge Management Process 2018;8(2): 1-9. doi: 10.5121/ijdkp.2018.8201##20. El_Jerjawi NS, Abu-Naser SS. Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology 2018; 21: 55-64. doi:10.14257/ijast.2018.121.05##21. Fitzpatrick LA. Secondary causes of osteoporosis. Mayo Clin Proc 2002;77(5):453-68. doi: 10.4065/77.5.453##22. Bogoch ER, Elliot-Gibson V, Wang RY, Josse RG. Secondary causes of osteoporosis in fracture patients. J Orthop Trauma 2012;26(9):e145-52. doi: 10.1097/BOT.0b013e3182323f2c##23. Raisz LG. Screening for osteoporosis. N Engl J Med 2005;353(2):164-71. doi: 10.1056/NEJMcp042092##24. Mäkitie RE, Costantini A, Kämpe A, Alm JJ, Mäkitie O. New insights into monogenic causes of osteoporosis. Front Endocrinol (Lausanne) 2019; 10: 70. doi: 10.3389/fendo.2019.00070##25. Zanker J, Duque G. Osteoporosis in older persons: old and new players. J Am Geriatr Soc 2019;67(4):831-40. doi: 10.1111/jgs.15716##26. Akin M, Eyduran E, Reed BM. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC) 2017;128(2):303-16. doi: 10.1007/s11240-016-1110-6##27. Milanović M, Stamenković M. CHAID decision tree: Methodological frame and application. Economic Themes 2016;54(4):563-86. doi: 10.1515/ethemes-2016-0029##28. Akin M, Eyduran E, Reed BM. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC). 2017;128(2):303-16. doi: 10.1007/s11240-016-1110-6##29. Zarkogianni K, Vazeou A, Mougiakakou SG, Prountzou A, Nikita KS. An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Trans Biomed Eng 2011;58(9):2467-77. doi: 10.1109/TBME.2011.2157823##30. Su MC. Use of neural networks as medical diagnosis expert systems. Computers in Biology and Medicine 1994;24(6):419-29. https://doi.org/10.1016/0010-4825(94)90040-X##31. Jaafar SF, Ali DM. Diabetes mellitus forecast using artificial neural network (ANN). Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research; 2005 Sep 5-7; Kuala Lumpur, Malaysia, Malaysia: IEEE; 2005. p. 135-9. doi: 10.1109/ASENSE.2005.1564523##32. Zurada JM. Introduction to Artificial Neural Systems. 1st ed.West Group; 1992.##33. Amiri Chaijan R, Khosh Taghaza M, Montazer GH, Minaee S, Alizadeh M. Estimation of head rice yield using artificial neural networks for fluidized bed drying of rough rice. Isfahan University of Technology-Journal of Crop Production and Processing 2009;13(48):285-98. [In Persian]##34. Wesolowski M, Suchacz B. Artificial neural networks: theoretical background and pharmaceutical applications: a review. J AOAC Int 2012;95(3):652-68. doi: 10.5740/jaoacint.sge_wesolowski_ann##35. Chatterjee SP, Pandya AS. Artificial Neural Networks in Drug Transport Modeling and Simulation–II. Artificial Neural Network for Drug Design, Delivery and Disposition 2016; 243-61. https://doi.org/10.1016/B978-0-12-801559-9.00012-0##36. Haykin S, Lippmann R. Neural networks, a comprehensive foundation. International Journal of Neural Systems 1994;5(4):363-4.##37. Wang W, Richards G, Rea S. Hybrid data mining ensemble for predicting osteoporosis risk. Engineering in Medicine and Biology 27th Annual Conference; 2006 Jan 17-18; Shanghai, China: IEEE; 2006. p. 886-9. doi: 10.1109/IEMBS.2005.1616557##38. Gao Z, Hong W, Xu Y, Zhang T, Song Z, Liu J. Osteoporosis diagnosis based on the multifractal spectrum features of micro-ct images and c4. 5 decision tree. First International Conference on Pervasive Computing, Signal Processing and Applications; 2010 Sep 17-19; Harbin, China: IEEE; 2010. p. 1043-47. doi: 10.1109/PCSPA.2010.257##39. Englebardt SP, Nelson R. Health Care Informatics: An Interdisciplinary Approach. 1st ed. St. Louis: Mosby; 2002.##1. Eastell R. Treatment of postmenopausal osteoporosis. N Engl J Med 1998;338(11):736-46. doi: 10.1056/NEJM199803123381107https://www.jri.ir/documents/fullpaper/fa/170.pdf##2. World Health Organization (WHO). Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [‎‎‎‎meeting held in Rome from 22 to 25 June 1992]. Available from: https://apps.who.int/iris/bitstream/handle/10665/39142/WHO_TRS_843.pdf?sequence=1 World Health Organization. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [‎‎‎meeting held in Rome from 22 to 25 June 1992]‎‎‎. World Health Organization; 1994.##3. Seeman E, Hopper JL, Bach LA, Cooper ME, Parkinson E, McKay J, et al. Reduced bone mass in daughters of women with osteoporosis. N Engl J Med 1989;320(9):554-8. doi: 10.1056/NEJM198903023200903##4. Slemenda CW, Christian JC, Williams CJ, Norton JA, Johnston CC. Genetic determinants of bone mass in adult women: a reevaluation of the twin model and the potential importance of gene interaction on heritability estimates. J Bone Miner Res 1991;6(6):561-7. doi: 10.1002/jbmr.5650060606##5. Bonjour JP, Theintz G, Law F, Slosman D, Rizzoli R. Peak bone mass. Osteoporos Int 1994;4:7-13. doi: 10.1007/BF01623429##6. Saadi HF, Reed RL, Carter AO, Qazaq HS, Al Suhaili AR. Bone density estimates and risk factors for osteoporosis in young women. East Mediterr Health J 2001;7(4-5):730-7.##7. Ruiz JC, Mandel C, Garabedian M. Influence of spontaneous calcium intake and physical exercise on the vertebral and femoral bone mineral density of children and adolescents. J Bone Miner Res 1995;10(5):675-82. doi: 10.1002/jbmr.5650100502##8. Rizzoli R, Bonjour JP, Ferrari SL. Osteoporosis, genetics and hormones. J Mol Endocrinol 2001;26(2):79-94. doi: 10.1677/jme.0.0260079##9. Bass S, Pearce G, Bradney M, Hendrich E, Delmas PD, Harding A, Seeman E. Exercise before puberty may confer residual benefits in bone density in adulthood: studies in active prepubertal and retired female gymnasts. J Bone Miner Res 1998;13(3):500-7. doi: 10.1359/jbmr.1998.13.3.500.##10. Ardeshir Larijani MB, Mohajeri Tehrani MR., Soltani Z, Pazhouhi M. Mosteoporosis, Prevention, Diagnosis and Treatment. Journal of Reproduction and Infertility 2005; 6(1): 5-24. [In Persian]##11. Sambrook PN, Dequiker J, Rasp HH. Metabolic bone disease, Report of a WHO study group, Assessment of fracture risk and its application to screening for postmenopausal Osteoporosis. Geneva: WHO Technical Report Series; 1994.##12. Lakshmi KV, Padmavathamma M. Modeling an expert system for diagnosis of gestational diabetes mellitus based on risk factors. Journal of Computer Engineering 2013;8(3):29-32. ##13. Stafford GC, Kelley PE, Syka JE, Reynolds WE, Todd JF. Recent improvements in and analytical applications of advanced ion trap technology. International Journal of Mass Spectrometry and Ion Processes 1984;60(1):85-98. https://doi.org/10.1016/0168-1176(84)80077-4##14. Vijiyarani S, Sudha S. Disease prediction in data mining technique–a survey. International Journal of Computer Applications & Information Technology 2013;2(1):17-21. ##15. Moudani W, Shahin A, Chakik F, Rajab D. Intelligent predictive osteoporosis system. International Journal of Computer Applications 2011;32(5):28-37. ##16. Zhou XH, Li SL, Tian F, Cai BJ, Xie YM, Pei Y, et al. Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors. Stat Med 2012;31(7):643-52. doi: 10.1002/sim.4382##17. Sharifkhani M, Alizadeh S, Abbasi M, Ameri H. Providing a Model for Predicting the Risk of Osteoporosis Using Decision Tree Algorithms. J Mazandaran Univ Med Sci 2014; 24(116):110-18. [In Persain] ##18. Sathawane MK, Tuteja MR. Data Mining in Clinical Records to foretell the risk of Osteoporosis; 2015.##19. Nahar N, Ara F. Liver disease prediction by using different decision tree techniques. International Journal of Data Mining & Knowledge Management Process 2018;8(2): 1-9. doi: 10.5121/ijdkp.2018.8201##20. El_Jerjawi NS, Abu-Naser SS. Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology 2018; 21: 55-64. doi:10.14257/ijast.2018.121.05##21. Fitzpatrick LA. Secondary causes of osteoporosis. Mayo Clin Proc 2002;77(5):453-68. doi: 10.4065/77.5.453##22. Bogoch ER, Elliot-Gibson V, Wang RY, Josse RG. Secondary causes of osteoporosis in fracture patients. J Orthop Trauma 2012;26(9):e145-52. doi: 10.1097/BOT.0b013e3182323f2c##23. Raisz LG. Screening for osteoporosis. N Engl J Med 2005;353(2):164-71. doi: 10.1056/NEJMcp042092##24. Mäkitie RE, Costantini A, Kämpe A, Alm JJ, Mäkitie O. New insights into monogenic causes of osteoporosis. Front Endocrinol (Lausanne) 2019; 10: 70. doi: 10.3389/fendo.2019.00070##25. Zanker J, Duque G. Osteoporosis in older persons: old and new players. J Am Geriatr Soc 2019;67(4):831-40. doi: 10.1111/jgs.15716##26. Akin M, Eyduran E, Reed BM. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC) 2017;128(2):303-16. doi: 10.1007/s11240-016-1110-6##27. Milanović M, Stamenković M. CHAID decision tree: Methodological frame and application. Economic Themes 2016;54(4):563-86. doi: 10.1515/ethemes-2016-0029##28. Akin M, Eyduran E, Reed BM. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC). 2017;128(2):303-16. doi: 10.1007/s11240-016-1110-6##29. Zarkogianni K, Vazeou A, Mougiakakou SG, Prountzou A, Nikita KS. An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Trans Biomed Eng 2011;58(9):2467-77. doi: 10.1109/TBME.2011.2157823##30. Su MC. Use of neural networks as medical diagnosis expert systems. Computers in Biology and Medicine 1994;24(6):419-29. https://doi.org/10.1016/0010-4825(94)90040-X##31. Jaafar SF, Ali DM. Diabetes mellitus forecast using artificial neural network (ANN). Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research; 2005 Sep 5-7; Kuala Lumpur, Malaysia, Malaysia: IEEE; 2005. p. 135-9. doi: 10.1109/ASENSE.2005.1564523##32. Zurada JM. Introduction to Artificial Neural Systems. 1st ed.West Group; 1992.##33. Amiri Chaijan R, Khosh Taghaza M, Montazer GH, Minaee S, Alizadeh M. Estimation of head rice yield using artificial neural networks for fluidized bed drying of rough rice. Isfahan University of Technology-Journal of Crop Production and Processing 2009;13(48):285-98. [In Persian]##34. Wesolowski M, Suchacz B. Artificial neural networks: theoretical background and pharmaceutical applications: a review. J AOAC Int 2012;95(3):652-68. doi: 10.5740/jaoacint.sge_wesolowski_ann##35. Chatterjee SP, Pandya AS. Artificial Neural Networks in Drug Transport Modeling and Simulation–II. Artificial Neural Network for Drug Design, Delivery and Disposition 2016; 243-61. https://doi.org/10.1016/B978-0-12-801559-9.00012-0##36. Haykin S, Lippmann R. Neural networks, a comprehensive foundation. International Journal of Neural Systems 1994;5(4):363-4.##37. Wang W, Richards G, Rea S. Hybrid data mining ensemble for predicting osteoporosis risk. Engineering in Medicine and Biology 27th Annual Conference; 2006 Jan 17-18; Shanghai, China: IEEE; 2006. p. 886-9. doi: 10.1109/IEMBS.2005.1616557##38. Gao Z, Hong W, Xu Y, Zhang T, Song Z, Liu J. Osteoporosis diagnosis based on the multifractal spectrum features of micro-ct images and c4. 5 decision tree. First International Conference on Pervasive Computing, Signal Processing and Applications; 2010 Sep 17-19; Harbin, China: IEEE; 2010. p. 1043-47. doi: 10.1109/PCSPA.2010.257##39. Englebardt SP, Nelson R. Health Care Informatics: An Interdisciplinary Approach. 1st ed. St. Louis: Mosby; 2002. ##

به کارگیری فیلتر وفقی برای حذف نویز از سیگنال‌های ECG با استفاده از تبدیل موجک و یادگیری عمیق Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning 1 1 مقدمه: الکتروکاردیاگرام یکی از روش‌های اندازه‌گیری فعالیت‌های الکتریکی قلب است که این اندازه‌گیری با قرار دادن الکترودهایی روی سطح بدن اندازه‌گیری می‌شود. پزشکان برای تشخیص و شناسایی بیماری‌های قلبی از ابزار مشاهده استفاده می‌کنند که این کار در سیگنال‌های ECG توسط متخصص قلب و عروق انجام می‌شود. به طور خاص بیماری‌های قلبی با بررسی نمایش گرافیکی سیگنال‌های قلبی که با عنوان ECG معرفی شد، انجام می‌شود. سیگنال‌های ECG به دلیل منابع خارجی یا سایر فرآیند‌های فیزیولوژیکی بدن انسان با نویز همراه می‌باشد. روش: در این پژوهش کاربردی یک فیلتر وفقی بر اساس تبدیل موجک و شبکه عصبی عمیق برای کاهش نویز پیشنهاد شد. این مجموعه ترکیبی از تبدیل ویولت، یادگیری وفقی و نگاشت غیر‌خطی از شبکه‌های عصبی عمیق است. شبکه عصبی عمیق به کمک فیلتر وفقی برای کاهش نویز بیشتر از سیگنال ECG مورد استفاده قرار می‌گیرد. نتایج: معیار مورد نظر برای ارزیابی کیفیت روش پیشنهادی به منظور حذف نویز، نسبت سیگنال به نویز می‌باشد که هدف این پژوهش افزایش این نسبت می‌باشد که بیانگر بازدهی روش مبتنی بر تبدیل موجک و یادگیری عمیق می‌باشد. نتیجه ­گیری: نتایج حاصل از شبیه‌سازی نشان می‌دهد که روش پیشنهادی نسبت به روش‌های موجود حدود 9/56 درصد حذف نویز از سیگنال ECG را بهبود می‌بخشد. علت این امر آن است که ضرایب استخراجی از فیلتر وفقی با استفاده از شبکه عصبی عمیق بهینه می‌شوند به گونه‌ای که شکل موج با نویز کمتری را فراهم می‌آورد. 2 Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by noise due to external sources or other physiological processes in the human body. Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the noise. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more noise in the ECG signal. Results: Signal-to-Noise ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove noise. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning. Conclusion: The results of the simulation showed that the proposed method improved the removal of noise from the ECG signal about 9.56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-noise waveform. 318 325 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/92019/11/22019/12/24 1398/10/3 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/112020/02/32020/05/11 1399/2/22 صابر فولادی Saber Fooladi دانشجوی دکتری مهندسی برق مخابرات، گروه مهندسی برق مخابرات ، دانشکده مهندسی برق و کامپیوتر ، پردیس شوکت آباد، دانشگاه بیرجند، بیرجند، ایران ایران saber.fooladi@birjand.ac.ir حسن فرسی Hassan Farsi دکترای تخصصی مهندسی برق مخابرات، استاد تمام، گروه مهندسی برق مخابرات ، دانشکده مهندسی برق و کامپیوتر ، پردیس شوکت آباد، دانشگاه بیرجند، بیرجند، ایران ایران hfarsi@birjand.ac.ir فریما فرسی Farima Farsi دانشجوی پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران ایران farima.farsi1999@yahoo.com ECG Signal Wavelet Transform Deep Learning سیگنال ECG تبدیل موجک یادگیری عمیق 1. Bahaz M, Benzid R. Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series. Australas Phys Eng Sci Med 2018;41(1):143-60. doi: 10.1007/s13246-018-0623-1 ##2. Sharma N, Singh Sidhu J. Removal of noise from ecg signal using adaptive filtering. Indian Journal of Science and Technology 2016;9(48). doi: 10.17485/ijst/2016/v9i48/106424##3. Sharma RR, Pachori RB. Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition. Biomedical Signal Processing and Control 2018;45:33–49. https://doi.org/10.1016/j.bspc.2018.05.002##4. Oliveira BR, Duarte MA, Abreu CC, Vieira Filho J. A wavelet-based method for power-line interference removal in ECG signals. Res Biomed Eng 2018;34(1):73–86. https://doi.org/10.1590/2446-4740.01817 ##5. Yazdanpanah B, Kumar KS, Raju GS. Notice of Removal: Noise removal ecg signal using non-adaptive filters and adaptive filter algorithm. 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مقایسه کمی زوج درخت‌های حاصل از درخت‌های فیلوژنتیکی ژن و پروتئین برای آنزیم سولفیت ردوکتاز فلاووپروتئینی آلفا، درخت‌های 5SrRNA و تاکسونومی در گونه‌های منتخب باکتریایی Quantitative Comparison of Tree Pairs Resulted from Gene and Protein Phylogenetic Trees for Sulfite Reductase Flavoprotein Alpha-Component and 5S rRNA and Taxonomic Trees in Selected Bacterial Species 1 1 مقدمه: خانواده پروتئینی FAD-FR دارای کوفاکتور FAD می‌باشد. یکی از آنزیم‌های مهم این خانواده سولفیت ردوکتاز فلاووپروتئینی آلفا می‌باشد. هدف این مطالعه توجه به کاربرد‌های این آنزیم در بیوتکنولوژی و صنعت این آنزیم در 19 گونه مشخص در ارتباط با تکامل است. روش: توالی‌های ژن و پروتئین سولفیت ردوکتاز فلاووپروتئینی آلفا، توالی‌های 5SrRNA و درخت تاکسونومی برای 19 گونه انتخابی باکتریایی استخراج شد. سپس به مقایسه کمی درخت‌های فیلوژنتیکی توالی‌های 5SrRNA، ژن و پروتئین با یکدیگر و با درخت تاکسونومی پرداخته شد. درخت‌های فیلوژنتیکی به کمک نرم‌افزار Mega7 و با استفاده از الگوریتم Neighbor joining رسم شد و درخت تاکسونومی با استفاده از Taxonomy Browser NCBI استخراج شد. نتایج: با مقایسه زوج درخت‌های مربوطه، درصد گونه‌های هم ارز و متوسط امتیاز هم ارزی گونه‌ها در هر زوج درخت محاسبه شد. زوج درخت ژن-پروتئین بالاترین امتیاز را در هر دو کمیت به خود اختصاص داد. در مقایسه درخت تاکسونومی با سه درخت دیگر، زوج درخت ژن-تاکسونومی بالاترین درصد را در متوسط امتیاز هم ارزی به دست آورد و زوج درخت پروتئین-تاکسونومی بالاترین درصد گونه‌های هم ارز را کسب کرد. نتیجه­ گیری: بر اساس پژوهش حاضر، می‌توان دریافت که مناسب‌ترین جایگزین برای هرکدام از درخت‌هایی که در این پژوهش برای بررسی روابط تکاملی محاسبه شده است کدام است. به بیان دیگر، کدام درخت تکاملی را می‌توان جایگزین درخت تکاملی دیگری کرد. 2 Introduction: FAD is the cofactor of FAD-FR protein family. Sulfite reductase flavoprotein alpha-component is one of the main enzymes of this family. Based on applications of this enzyme in biotechnology and industry, it was chosen as the subject of evolutionary studies in 19 specific species. Method: Gene and protein sequences of sulfite reductase flavoprotein alpha-component, 5S rRNA sequences, and taxonomic tree were extracted from 19 selected bacterial species. Then, phylogenetic trees of 5S rRNA and gene and protein sequences were compared with each other and with taxonomic tree. Phylogenetic trees were drawn by Mega7 software using neighbor-joining algorithm and taxonomic tree was extracted using NCBI taxonomy browser. Results: By comparing the corresponding tree pairs, the percentage of equivalent species and the mean equivalence score of species were calculated for each tree pair. The gene-protein tree was allocated the highest scores in both quantities. In comparing the taxonomic tree with three other trees, gene-taxonomy tree achieved the highest percentage in the mean equivalence score and protein-taxonomy tree obtained the highest percentage of equivalent species. Conclusion: Based on the results of the present research, the best replacement for each of the trees investigated in this study regarding evolutionary relations was identified. In other words, this study helps detect which evolutionary tree can be replaced for another evolutionary tree. 326 336 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/92019/11/22019/12/242019/11/29 1398/9/8 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/112020/02/32020/05/112020/01/11 1398/10/21 الهام ترحمی Elham Tarahomi کارشناس ارشد ژنتیک مولکولی، گروه ژنتیک، دانشکده علوم و فناوری‌های نوین، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران ایران elhamtarahomi@gmail.com حسین فهیمی Hossein Fahimi دکتری ژنتیک مولکولی، استادیار، گروه ژنتیک، دانشکده علوم و فناوری‌های نوین، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران ایران h.fahimi@iaups.ac.ir محمد تقی زاده Mohammad Taghizadeh دکتری بیوانفورماتیک، استاد مدعو، گروه بیوتکنولوژی، دانشکده علوم و فناوری‌های نوین، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران ایران mtaghizadeh@Alumni.ut.ac.ir Phylogenetic Tree Taxonomic Tree Sulfite Reductase Flavoprotein Alpha-Component Enzyme 5S rRNA Gene Tree Pair درخت فیلوژنتیکی درخت تاکسونومی آنزیم سولفیت ردوکتاز فلاووپروتئینی آلفا ژن 5SrRNA‌ زوج درخت 1. 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بررسی و تحلیل نقاط قوت، ضعف، فرصت و تهدیدهای فناوری‌های الکترونیک پوشیدنی: مطالعه مروری سیستماتیک The Investigation and Analysis of the Strengths, Weaknesses, Opportunities, and Threats of Wearable Electronic Technologies: A Systematic Review 1 1 مقدمه: ابزارهای الکترونیک‌های پوشیدنی که بر پایه فناوری اینترنت اشیاء و محاسبات کلان داده استوارند، قادر به جمع‌آوری مستمر، پردازش اطلاعات فیزیولوژیک و محیطی و مبادله آن‌ها با سایر ابزارها، کاربران و شبکه‌های اینترتی هستند؛ لذا علیرغم مزیت‌های بالقوه در حوزه پایش سلامت، مخاطراتی جدی خصوصاً مرتبط با نقض حریم خصوصی نیز به همراه دارند. از این رو سؤال اصلی در این بررسی شناسایی مهم‌ترین نقاط قوت، ضعف، فرصت و تهدیدهای مرتبط با فناوری‌های الکترونیک پوشیدنی بود. روش: در این بررسی از نرم‌افزار StArt 3.4 جهت انجام مرور سیستماتیک استفاده شد. جستجوی منابع تا تاریخ 9 آذر 1398 در پایگاه‌های Scopus, IEEE, PubMed, Springer, Magiran, SID, Civilica و موتور جستجوی Google scholar انجام شد. نتایج: پس از حذف منابع تکراری و نامرتبط، 80 منبع جهت بررسی نهایی انتخاب و با استفاده از آمار توصیفی تحلیل شدند. «ارتقاء سبک زندگی و توانمندی‌های انسانی»، «کیفیت پایین رابط کاربری و داده‌های نامطمئن»، «اهمیت کاربردی در حوزه پزشکی و مراقبت سلامت» و «سرقت اطلاعات و نقض حریم خصوصی» به ترتیب با فراوانی 97/5%، 92/5%، 94‌% و 99‌% به عنوان مهم‌ترین نقاط قوت، ضعف، فرصت و تهدید شناسایی شدند. نتیجه‌گیری: نتایج این بررسی نشان داد نیروی محرکه اصلی در توسعه فناوری‌های الکترونیک پوشیدنی، قابلیت‌های آن در ارتقاء توانمندی‌های انسان و کاربرد در حوزه پزشکی و مراقبت سلامت است؛ لذا برای بهره‌برداری از فرصت و غلبه بر تهدیدهای بالقوه این فناوری، توسعه و به کارگیری دانش بومی و همچنین برنامه‌ریزی و تدوین استانداردها و قوانین مورد نیاز باید سریعاً در دستور کار قرار گیرد. 2 Introduction: Wearable electronic devices, which are based on Internet of Things (IoT) and big data computing, are able to continuously collect and process the physiological and environmental data and exchange them with other tools, users, and internet networks. Therefore, despite their potential benefits in health monitoring, they can pose serious risks, especially in breach of privacy. Hence, the main question in this study was to identify the most important strengths, weaknesses, opportunities, and threats related to wearable electronic technologies. Method: In this study, StArt 3.4 software was used for systematic review. Studies until November 30, 2019 were searched for keywords in “Scopus”, “IEEE”, “PubMed”, “Springer”, “Magiran”, “SID”, and “Sivilica” databases and Google search engine. Results: After deleting duplicate and unrelated documents, 80 documents were selected for final review and were analyzed using descriptive statistics. Accordingly, the main identified strength, weakness, opportunity, and threat were “improving lifestyle and human capabilities”, “low data reliability and user interface”, “applications in health and medicine”, and “information abuse and privacy breach” with 97.5%, 92.5%, 94%, and 99% frequency, respectively. Conclusion: The results of this study showed that improving human capabilities and application in medicine and health care are the main driving forces for the development of wearable electronic technologies. Therefore, in order to take advantage of the opportunities and overcome the potential threats of this technology, planning for the development and application of indigenous knowledge, as well as the development of the required standards and rules, must be put on the agenda immediately 337 350 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/92019/11/22019/12/242019/11/292019/12/14 1398/9/23 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/112020/02/32020/05/112020/01/112020/05/11 1399/2/22 زیدالدین پشندی Zaiddodine Pashandi دکتری تخصصی بیوفیزیک، پژوهشگر همکار، گروه علوم و فناوری‌های دفاعی، پژوهشکده آماد، دانشگاه و پژوهشگاه عالی دفاع ملی و تحقیقات راهبردی، تهران، ایران ایران Pashandiz@yahoo.com حنیف کازرونی Hanif Kazerooni دکتری تخصصی مهندسی شیمی، استادیار، گروه علوم و فناوری‌های دفاعی، پژوهشکده آماد، دانشگاه و پژوهشگاه عالی دفاع ملی و تحقیقات راهبردی، تهران، ایران ایران hanif.kazerooni@gmail.com حسین حسن پور Hossein Hassanpoor دکتری تخصصی مهندسی پزشکی، استادیار، گروه علوم و فناوری‌های دفاعی، پژوهشکده آماد، دانشگاه و پژوهشگاه عالی دفاع ملی و تحقیقات راهبردی، تهران، ایران ایران hassanpoor@elenoon.ir Wearable Electronic Technologies Strengths Weaknesses Opportunities Threats SWOT analysis فناوری‌های الکترونیک پوشیدنی قوت ضعف فرصت‌ها تهدیدها تحلیل SWOT 1. 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وجود داده‌های گمشده در مجموعه داده PIMA و عدم توجه به آن در مقاله پذیرفته شده در آن مجله Missing Values in PIMA Diabetes Dataset and Not Considering It in an Article Accepted in that Journal 1 1 مقاله از نوع نامه به سردبیر است لذا فاقد چکیده می باشد. 2 The article is a letter to the editor, so it has no abstract. 351 353 2020/10/312019/10/82019/07/142019/10/292019/12/52019/10/232019/11/92019/11/22019/12/242019/11/292019/12/142019/08/31 1398/6/9 2020/11/32020/01/82020/02/32020/04/182020/05/112020/07/202020/01/112020/02/32020/05/112020/01/112020/05/112019/09/14 1398/6/23 فاطمه آهوز Fatemeh Ahouz مربی، گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه صنعتی خاتم‌الانبیاء بهبهان، بهبهان، ایران ایران fatemeh.ahouz@gmail.com امین گلاب پور Amin Golabpour استادیار، گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شاهرود، شاهرود، ایران ایران a.golabpour@gmail.com Diabetes Missing Values PIMA dataset data mining دیابت مقادیر گمشده مجموعه داده PIMA داده‌کاوی 1. 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