LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,224
تعداد مقالات11,159
تعداد مشاهده مقاله76,918,660
تعداد دریافت فایل اصل مقاله101,109,809
Helmi, Nona, Veisi, Gelareh. (1404). Social Network Analysis and Behavioral Insights to Enhance Patient Engagement in Chiropractic Care: A Quasi-Experimental Study Using Virtual Reality and Deep Learning. سامانه مدیریت نشریات علمی, (), -. doi: 10.30476/ijvlms.2025.104915.1323
Nona Helmi; Gelareh Veisi. "Social Network Analysis and Behavioral Insights to Enhance Patient Engagement in Chiropractic Care: A Quasi-Experimental Study Using Virtual Reality and Deep Learning". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.30476/ijvlms.2025.104915.1323
Helmi, Nona, Veisi, Gelareh. (1404). 'Social Network Analysis and Behavioral Insights to Enhance Patient Engagement in Chiropractic Care: A Quasi-Experimental Study Using Virtual Reality and Deep Learning', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.30476/ijvlms.2025.104915.1323
Helmi, Nona, Veisi, Gelareh. Social Network Analysis and Behavioral Insights to Enhance Patient Engagement in Chiropractic Care: A Quasi-Experimental Study Using Virtual Reality and Deep Learning. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.30476/ijvlms.2025.104915.1323

Social Network Analysis and Behavioral Insights to Enhance Patient Engagement in Chiropractic Care: A Quasi-Experimental Study Using Virtual Reality and Deep Learning

Interdisciplinary Journal of Virtual Learning in Medical Sciences
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 02 مهر 1404    اصل مقاله (2.59 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/ijvlms.2025.104915.1323
نویسندگان
Nona Helmi1؛ Gelareh Veisi* 2
1Department of Computer Engineering, Ne.C., Islamic Azad University, Neyshabour, Iran
2Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran
چکیده
Background: Chiropractic care is widely applied for pain relief and musculoskeletal improvement, yet limited emotional support and one-way communication may reduce trust and engagement. This quasi-experimental study aimed to examine the effect of a mobile Augmented Reality Application (ARA), combined with a Convolutional Neural Network (CNN) predictive model, on enhancing patient satisfaction, adherence, and clinical outcomes within chiropractic care.
Methods: A pre-test post-test quasi-experimental design was implemented from January to December 2022 in a private chiropractic clinic in Mashhad, Iran. Out of 73 eligible patients with musculoskeletal disorders, 51 completed the study, with low back pain and neck pain being the most common issues reported. Participants were randomly assigned to an intervention group (n=21; standard care + ARA app) or control group (n=30; standard care only). Outcomes included functional performance (mental health, vitality, social functioning, disability, and kinesiophobia), treatment satisfaction, and adherence. Assessments were conducted at baseline and weeks 3, 5, and 8 using validated tools, including the 12-item Short Form Health Survey Questionnaire (SF-12) and the Fear Avoidance and Belief Questionnaire (FABQ). The satisfaction rate was assessed at week 8 using a 10-item questionnaire developed by the research team, while treatment adherence was monitored by app usage in the intervention group or clinic attendance in the control group.
Results: By the eighth week, the intervention group reported higher satisfaction (91.6±6.5) compared to the control group (63.3±12.5; p<0.01). Significant improvements were observed in functional performance measures, including mental health, vitality, social functioning, and fear avoidance (p<0.01). A positive correlation was also found between adherence and satisfaction (r=0.402, p=0.003). The CNN model demonstrated moderate predictive accuracy with a Root Mean Squared Error (RMSE) of 0.1121 and a correlation coefficient of 0.1491 (p<0.01).
Conclusion: The ARA app significantly improved outcomes, suggesting a scalable, patient-centered digital strategy. However, further extensive and long-term trials are recommended to validate its scalability.

تازه های تحقیق

Nona Helmi (Google Scholar)

Gelareh Veisi (Google Scholar)

کلیدواژه‌ها
Chiropractic؛ Mobile Applications؛ Neural Networks؛ Patient Satisfaction؛ Musculoskeletal Pain؛ Augmented Reality؛ Telemedicine
مراجع
  1. ILO. Recommendation concerning the list of occupational diseases and the recording and notification of occupational accidents and diseases (Recommendation R194). Geneva; 2002.
  2. Parno A, Sayehmiri K, Parno M, Khandan M, Poursadeghiyan M, Maghsoudipour M, Ebrahimi MH. The prevalence of occupational musculoskeletal disorders in Iran: A meta-analysis study. Work. 2017;58(2):203-14. doi: 10.3233/WOR-172619. PubMed PMID: 29036866.
  3. Waehrer G, Leigh JP, Miller TR. Costs of occupational injury and illness within the health services sector. Int J Health Serv. 2005;35(2):343-59. doi: 10.2190/RNQ3-0C13-U09M-TENP. PubMed PMID: 15934169.
  4. Parno A, Sayehmiri K, Nabi Amjad R, H.Ahagh MM, Hosseini Foladi Sh, Khammar Ar, Poursadeqiyan M. Meta-analysis Study of Work-related Musculoskeletal Disorders in Iran. Rehabiliation. 2020;21(2):182-205. doi: 10.32598/RJ.21.2.2444.4.
  5. Nelson CF, Metz RD, LaBrot T. Effects of a managed chiropractic benefit on the use of specific diagnostic and therapeutic procedures in the treatment of low back and neck pain. J Manipulative Physiol Ther. 2005;28(8):564-9. doi: 10.1016/j.jmpt.2005.08.010. PubMed PMID: 16226623.
  6. Dagenais S. Why not everyone with low back pain chooses chiropractic care. J Can Chiropr Assoc. 2013;57(1):6-9. PubMed PMID: 23476079; PubMed Central PMCID: PMC3580998.
  7. Gevers-Montoro C, Deldar Z, Conesa-Buendía FM, Lazar EA, Mahillo-Fernandez I, Khatibi A, Ortega de Mues A. Pain catastrophizing mediates rapid benefits of accessing in-person chiropractic care during the COVID-19 lockdown. Eur J Pain. 2022;26(2):463-479. doi: 10.1002/ejp.1872. PubMed PMID: 34618991; PubMed Central PMCID: PMC8653257.
  8. Perrin A. Mobile technology and home broadband: Pew Research Center; 2021 June 3. [Cited 2024 October 12]. Available from: https://www.pewresearch.org/internet/2021/06/03/mobile-technology-and-home-broadband-2021/.
  9. Ng JY, Verhoeff N, Steen J. What are the ways in which social media is used in the context of complementary and alternative medicine in the health and medical scholarly literature? a scoping review. BMC Complement Med Ther. 2023;23(1):32. doi: 10.1186/s12906-023-03856-6. PubMed PMID: 36732809; PubMed Central PMCID: PMC9893203.
  10. National Guideline Centre (UK). Low Back Pain and Sciatica in Over 16s: Assessment and Management. London: National Institute for Health and Care Excellence (NICE); 2016 Nov. PubMed PMID: 27929617.
  11. WFC. 20 Principles; Advancing awarness, utilization and integration of chiropractic internationally. Toronto, Canada: World Federation of Chiropractic 2023. Available from: https://www.wfc.org/website/documents/799_20-principles-2023.pdf.
  12. Cottrell MA, Galea OA, O'Leary SP, Hill AJ, Russell TG. Real-time telerehabilitation for the treatment of musculoskeletal conditions is effective and comparable to standard practice: a systematic review and meta-analysis. Clin Rehabil. 2017;31(5):625-38. doi: 10.1177/0269215516645148. PubMed PMID: 27141087.
  13. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42. doi: 10.1186/1748-5908-6-42. PubMed PMID: 21513547; PubMed Central PMCID: PMC3096582.
  14. Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, Patel V, Haines A. The Effectiveness of Mobile-health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-analysis. PLoS Med. 2013;10(1):e1001363. doi: 10.1371/journal.pmed.1001363. PubMed PMID: 23458994; PubMed Central PMCID: PMC3566926.
  15. RAND. 12-Item Short Form Survey (SF-12) 2020. [Cited 2024 Oct 22]. Available from: https://www.rand.org/health/surveys/mos/12-item-short-form.html.
  16. Luo X, George ML, Kakouras I, Edwards CL, Pietrobon R, Richardson W, Hey L. Reliability, validity, and responsiveness of the short form 12-item survey (SF-12) in patients with back pain. Spine (Phila Pa 1976). 2003;28(15):1739-45. doi: 10.1097/01.BRS.0000083169.58671.96. PubMed PMID: 12897502.
  17. 17. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-33. doi: 10.1097/00005650-199603000-00003. PubMed PMID: 8628042.
  18. Waddell G, Newton M, Henderson I, Somerville D, Main CJ. A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of fear-avoidance beliefs in chronic low back pain and disability. Pain. 1993;52(2):157-168. doi: 10.1016/0304-3959(93)90127-B. PubMed PMID: 8455963.
  19. Mintken PE, Cleland JA, Whitman JM, George SZ. Psychometric properties of the Fear-Avoidance Beliefs Questionnaire and Tampa Scale of Kinesiophobia in patients with shoulder pain. Arch Phys Med Rehabil. 2010;91(7):1128-36. doi: 10.1016/j.apmr.2010.04.009. PubMed PMID: 20599053.
  20. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. doi: 10.1038/nature14539. PubMed PMID: 26017442.
  21. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102-27. doi: 10.1016/j.zemedi.2018.11.002. PubMed PMID: 30553609.
  22. Dominik Scherer AMSB. Evaluation of pooling operations in convolutional architectures for object recognition. Artificial Neural Networks – ICANN 2010; Berlin: Springer; 2010. p. 99-101.
  23. Y. LeCun BB, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation. 1989;1(4):541-51.
  24. Hao R, Namdar K, Liu L, Haider MA, Khalvati F. A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks. J Digit Imaging. 2021;34(4):862-76. doi: 10.1007/s10278-021-00478-7. PubMed PMID: 34254200; PubMed Central PMCID: PMC8455796.
  25. Jia Deng WD, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. ImageNet: A large-scale hierarchical image database. Conference on Computer Vision and Pattern Recognition; 2009 June 20-25; Miami, FL, USA. USA: IEEE; 2009.P. 248-55, doi:10.1109/CVPR.2009.5206848.
  26. Ramprasaath R.Selvaraju MC, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra., editor Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization. Proceedings of the International Conference on Computer Vision (ICCV); 2017 Oct 22-29; Venice, Italy. USA: IEEE; 2017. doi: 10.1109/ICCV.2017.74.
  27. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1):e001570. doi: 10.1136/bmjopen-2012-001570. PubMed PMID: 23293244; PubMed Central PMCID: PMC3549241.
  28. Luxton DD, McCann RA., Bush NE, Mishkind MC, Reger GM. mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Prof Psychol Res Pr. 2011;42(6):505–12. doi:10.1037/a0024485.
  29. Gitto S, Serpi F, Albano D, Risoleo G, Fusco S, Messina C, Sconfienza LM. AI applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp. 2024;8(1):22. doi: 10.1186/s41747-024-00422-8. PubMed PMID: 38355767; PubMed Central PMCID: PMC10866817.
  30. Yeo G, Fortuna KL, Lansford JE, Rudolph KD. The effects of digital peer support interventions on physical and mental health: a review and meta-analysis. Epidemiol Psychiatr Sci. 2025;34:e9. doi: 10.1017/S2045796024000854. PubMed PMID: 39945388; PubMed Central PMCID: PMC11886969.
  31. Birrell L, Debenham J, Furneaux-Bate A, Prior K, Spallek S, Thornton L, Chapman C, Newton N. Evaluating a Peer-Support Mobile App for Mental Health and Substance Use Among Adolescents Over 12 Months During the COVID-19 Pandemic: Randomized Controlled Trial. J Med Internet Res. 2023;25:e45216. doi: 10.2196/45216. PubMed PMID: 37756116; PubMed Central PMCID: PMC10538359.
  32. Paris Ferrer T, Masaracchio M, Kirker K, Madi Dewan B, Manthripragada M, Ojha H. Outcomes of direct access telehealth physical therapy for patients with musculoskeletal pain: a single cohort observational retrospective study. Physiother Theory Pract. 2024;40(10):2233-2240. doi: 10.1080/09593985.2023.2245032. PubMed PMID: 37585711.
  33. Tran S, Smith L, Carter S. Understanding Patient Perspectives on the Use of Gamification and Incentives in mHealth Apps to Improve Medication Adherence: Qualitative Study. JMIR Mhealth Uhealth. 2024;12:e50851. doi: 10.2196/50851. PubMed PMID: 38743461; PubMed Central PMCID: PMC11134245.
  34. Kruse CS, Mileski M, Moreno J. Mobile health solutions for the aging population: A systematic narrative analysis. J Telemed Telecare. 2017;23(4):439-51. doi: 10.1177/1357633X16649790. PubMed PMID: 27255207.
  35. Amagai S, Pila S, Kaat AJ, Nowinski CJ, Gershon RC. Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review. J Med Internet Res. 2022;24(4):e35120. doi: 10.2196/35120. PubMed PMID: 35471414; PubMed Central PMCID: PMC9092233.
  36. Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J Technol Behav Sci. 2020;5(3):245-257. doi: 10.1007/s41347-020-00134-x. PubMed PMID: 33415185; PubMed Central PMCID: PMC7785056.
  37. Gabriela Villalobos-Zúñiga MC. Apps That Motivate: A Taxonomy of App Features Based on Self-Determination Theory. Int J Hum Comput Stud. 2020;140. doi: 10.1016/j.ijhcs.2020.102449.
  38. Milne-Ives M, Homer SR, Andrade J, Meinert E. Potential associations between behavior change techniques and engagement with mobile health apps: a systematic review. Front Psychol. 2023;14:1227443. doi: 10.3389/fpsyg.2023.1227443. PubMed PMID: 37794916; PubMed Central PMCID: PMC10545861.
  39. Yae Won Tak JWL, Junetae Kim, Yura Lee Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices.J Med Internet Res. 2024;26:e59444. doi: 10.2196/59444. PubMed PMID: 39250192. PubMed Central PMCID: 11420572.
  40. Baroni MP, Jacob MFA, Rios WR, Fandim JV, Fernandes LG, Chaves PI, Fioratti I, Saragiotto BT. The state of the art in telerehabilitation for musculoskeletal conditions. Arch Physiother. 2023;13(1):1. doi: 10.1186/s40945-022-00155-0. PubMed PMID: 36597130; PubMed Central PMCID: PMC9810517.
  41. Aschentrup L, Steimer PA, Dadaczynski K, Mc Call T, Fischer F, Wrona KJ. Effectiveness of gamified digital interventions in mental health prevention and health promotion among adults: a scoping review. BMC Public Health. 2024;24(1):69. doi: 10.1186/s12889-023-17517-3. PubMed PMID: 38167010; PubMed Central PMCID: PMC10763397.
  42. Wang Y, Min J, Khuri J, Xue H, Xie B, A Kaminsky L, J Cheskin L. Effectiveness of Mobile Health Interventions on Diabetes and Obesity Treatment and Management: Systematic Review of Systematic Reviews. JMIR Mhealth Uhealth. 2020;8(4):e15400. doi: 10.2196/15400. PubMed PMID: 32343253; PubMed Central PMCID: PMC7218595.
  43. Fatoye F, Gebrye T, Mbada C, Useh U. Economic Evaluations of Digital Health Interventions for the Management of Musculoskeletal Disorders: Systematic Review and Meta-Analysis. J Med Internet Res. 2023;25:e41113. doi: 10.2196/41113. PubMed PMID: 37410542; PubMed Central PMCID: PMC10359913.
  44. Reimer LM, Nissen L, von Scheidt M, Perl B, Wiehler J, Najem SA, Limbourg FP, Tacke T, Müller A, Jonas S, Schunkert H, Starnecker F. User-centered development of an mHealth app for cardiovascular prevention. Digit Health. 2024;10:20552076241249269. doi: 10.1177/20552076241249269. PubMed PMID: 38774157; PubMed Central PMCID: PMC11107323.
آمار
تعداد مشاهده مقاله: 3
تعداد دریافت فایل اصل مقاله: 1


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب