Fizyoterapi Mobil Kabul Anketi Türkçe Versiyonunun Geçerliliği ve Güvenirliği

Yıl 2024, Cilt: 4 Sayı: 2, 799 - 811, 27.08.2024

Metehan Yana , Musa Güneş , Ahmet Düha Koç , Volkan Temizkan

Öz

Amaç: Bu çalışmanın amacı Fizyoterapi Mobil Kabul Anketi'nin (PTMAQ) geçerlik ve güvenirliğini incelemektir. Yöntem: Türkiye'deki sağlık kurumlarında aktif olarak çalışan toplam 421 fizyoterapistten kolayda örnekleme yoluyla toplanan verilerin analizinde yapısal eşitlik modellemesi kullanıldı. Bulgular: Ölçekte PEOU ile ilgili ters kodlanan soruların revize edilerek olumlu ifadelere dönüştürülmesiyle güvenirlik artacaktır. Ayrıca Spesifik Klinik Amaçlar için mSağlık Aracı Önerilme Olasılığı (LRMH) ölçeğini oluşturan "yürüyüş hızı", "yürüyüş kalitesi ve denge" ile "ağrı ve bilişsel durum" boyutları aynı yapıyı ölçtüğü için tek boyutta toplanması gerektiği görülmüştür. Ayrıca anketin üçüncü bölümünde LRMH ölçeğini oluşturan boyutlardan ACTIV1, GAITQUAL3, BALANCE1 ve PAIN3 ifadelerinin faktör yapısını bozduğu için çıkarılmasının uygun olacağı düşünülmektedir. SPEED1, GAITQUAL1 ve PAIN1 ifadeleri ise aynı yapı içerisinde benzer durumları ölçen ifadelerdir. Sonuç: PTMAQ'da yapılacak revizyonlar sonucunda daha geçerli ve güvenilir bir ölçme aracının elde edileceği öngörülmektedir.

Anahtar Kelimeler

Sağlık, Mobil sağlık uygulamaları, Fizyoterapi mobil kabul anketi, Teknoloji değerlendirmesi, Geçerlilik

Validity and Reliability of Turkish Version of Physiotherapy Mobile Acceptance Questionnaire

Öz

Objective: The aim of this study is to examine the validity and reliability of the Physiotherapy Mobile Acceptance Questionnaire (PTMAQ). Method: Structural equation modeling was used to analyze data collected by convenience sampling from a total of 421 physiotherapists actively working in health institutions in Turkey. Results: The reliability will increase when the reverse coded questions in the scale related to PEOU are revised and converted into positive statements. In addition, since the "Gait speed", "Gait Quality and balance" and "Pain/cognitive status" dimensions that make up the Likelihood of Recommending an mHealth Tool for Specific Clinical Purposes (LRMH) scale measure the same structure, it was seen that they should be collected in one dimension. In addition, it is thought that it would be appropriate to remove the ACTIV1, GAITQUAL3, BALANCE1, PAIN3 expressions, which are among the dimensions that make up the LRMH scale in the third part of the questionnaire, because they distort the factor structure, and the SPEED1, GAITQUAL1, PAIN1 expressions are expressions that measure similar situations within the same structure. Conclusions: It is predicted that a more valid and reliable measurement tool will be obtained as a result of the revisions to be made in the PTMAQ.

Anahtar Kelimeler

Health, Mobile health apps, Physiotherapy mobile acceptance questionnaire, Technology assessment, Validity.

Kaynakça

• Alam, M. Z., Hu, W., Kaium, M. A., Hoque, M. R., & Alam, M. M. D. (2020). Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society, 61, 101255. https://doi.org/10.1016/j.techsoc.2020.101255
• Blumenthal, J., Wilkinson, A., & Chignell, M. (2018). Physiotherapists’ and Physiotherapy Students’ Perspectives on the Use of Mobile or Wearable Technology in Their Practice. Physiother Can, 70(3), 251–261. https://doi.org/10.3138/ptc.2016-100.e
• Brown, M., & Cudeck, R. (1993). Testing Structural Equation Models. In: Bollen, K. A., & Long, J. S. (Ed.), Bollen 1993 testing. SAGE.
• Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
• Gagnon, M. P., Orruño, E., Asua, J., Abdeljelil, A. B., & Emparanza, J. (2012). Using a Modified Technology Acceptance Model to Evaluate Healthcare Professionals’ Adoption of a New Telemonitoring System. Telemed J E-Health, 18(1), 54–59. https://doi.org/10.1089/tmj.2011.0066
• Gefen, D., Straub, D., & Boudreau, M.C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4. https://doi.org/10.17705/1CAIS.00407
• George, D., & Mallery, P. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference. Allyn & Bacon Publishers.
• Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186–192. https://doi.org/10.1177/002224378802500207
• Glegg, S. M. N., Holsti, L., Velikonja, D., Ansley, B., Brum, C., & Sartor, D. (2013). Factors Influencing Therapists’ Adoption of Virtual Reality for Brain Injury Rehabilitation. Cyberpsychol, Behav Soc Netw, 16(5), 385–401. https://doi.org/10.1089/cyber.2013.1506
• Ho, K. (2013). Health-e-Apps: A project to encourage effective use of mobile health applications. BC Medical Journal, 55(10), 458–460.
• Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
• Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a Reliability Coefficient be too High? Journal of Consumer Psychology, 10(1/2), 55–58.
• Joseph F. H., William C. B., Barry J. B., & Rolph E. A. (2013). Multivariate Data Analysis. Pearson Education Limited. Keel, S., Schmid, A., Keller, F., & Schoeb, V. (2023). Investigating the use of digital health tools in physiotherapy: facilitators and barriers. Physiother Theory Pract, 39(7), 1449–1468. https://doi.org/10.1080/09593985.2022.2042439
• MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99. https://doi.org/10.1037/1082-989X.4.1.84
• Nunnally, J. C. (1978). Psychometric theory. McGraw-Hall.
• Palos-Sanchez, P. R., Saura, J. R., Rios Martin, M. Á., & Aguayo-Camacho, M. (2021). Toward a Better Understanding of the Intention to Use mHealth Apps: Exploratory Study. JMIR MHealth and UHealth, 9(9), e27021. https://doi.org/10.2196/27021
• Rai, A., Chen, L., Pye, J., & Baird, A. (2013). Understanding Determinants of Consumer Mobile Health Usage Intentions, Assimilation, and Channel Preferences. J Med Internet Res, 15(8), e149. https://doi.org/10.2196/jmir.2635
• Sezgin, E., Özkan-Yildirim, S., & Yildirim, S. (2018). Understanding the perception towards using mHealth applications in practice. Information Development, 34(2), 182–200. https://doi.org/10.1177/0266666916684180
• Shiferaw, K. B., & Mehari, E. A. (2019). Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked, 17, 100182. https://doi.org/10.1016/j.imu.2019.100182
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
• Wu, J.H., Wang, S.C., & Lin, L.M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. Int J Med Inform, 76(1), 66–77. https://doi.org/10.1016/j.ijmedinf.2006.06.006