Journals / Etkili Hemşirelik Dergisi / 2026 / Cilt: 19 Sayı: 3

Development of the ITAKI II Fall Risk Assessment Scale and Evaluation of Its Psychometric Properties

İTAKİ II Düşme Riski Değerlendirme Ölçeği’nin Geliştirilmesi ve Psikometrik Özelliklerinin Değerlendirilmesi

Veysel Barış(DOKUZ EYLÜL ÜNİVERSİTESİ)
Şeyda Seren İntepeler(DOKUZ EYLUL UNIVERSITY)
İsa Çelik(RECEP TAYYIP ERDOGAN UNIVERSITY)0000-0003-3381-2286
Murat Bektaş(DOKUZ EYLUL UNIVERSITY)0000-0003-3327-8204
Şerife Engeloğlu(Ministry of Health)
Pages
383–396
Publication Date
2026-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Its structure, which aligns with the ICF model, enhances comparability of data at both national and international levels and contributes to patient safety practices.

📌 3 Key Takeaways

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Objective & Hypothesis:Background: The key to preventing patient falls, which can cause severe injuries, is identifying high-risk individuals, yet current assessment tools remain limited in accuracy.
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Key Findings & Data:Methods: The study used a methodological, cross-sectional design involving 1,173 patients aged 18+ across 11 hospitals in Türkiye.
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Conclusion & Impact:Its structure, which aligns with the ICF model, enhances comparability of data at both national and international levels and contributes to patient safety practices.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Hasta Düşmeleri · Risk Değerlendirme · İTAKİ

Abstract

Background: The key to preventing patient falls, which can cause severe injuries, is identifying high-risk individuals, yet current assessment tools remain limited in accuracy. Objectives: To develop a tool for assessing fall risk in adult patients and evaluate its validity and reliability. Methods: The study used a methodological, cross-sectional design involving 1,173 patients aged 18+ across 11 hospitals in Türkiye. The risk assessment tool was developed based on a literature review and expert opinions, with its content validity evaluated using the Content Validity Index. Inter-rater reliability was measured using the Kappa statistic, and internal consistency was assessed using the Kuder-Richardson 20 (KR-20) coefficient. The tool’s discriminative power was evaluated through Receiver Operating Characteristic (ROC) analysis. The risk factors included in the developed tool were classified according to the World Health Organization’s International Classification of Functioning, Disability, and Health Model (ICF). The developed risk assessment tool was called ITAKI II Fall Risk Assessment Tool. Results: The developed risk assessment tool included a total of 13 risk factors. The tool demonstrated a Content Validity Index of .963, an area under the ROC curve value of .809, an inter-rater agreement (Kappa) of .91, and an internal consistency coefficient (Kuder-Richardson 20) of .568. Conclusion: The newly developed ITAKI II Fall Risk Assessment Tool effectively identifies patients at high risk of falling. Its structure, which aligns with the ICF model, enhances comparability of data at both national and international levels and contributes to patient safety practices.

Özet

Giriş: Hastalarda ciddi yaralanmalara neden olan düşmelerin önlenebilmesi için düşme riski yüksek hastaların belirlenmesi öncelikli girişimdir. Ancak mevcut risk değerlendirme araçları bu konuda sınırlılık göstermektedir. Amaç: Yetişkin hastaların risk değerlendirmesinde kullanılacak bir aracın geliştirilmesi, geçerlik ve güvenirliğinin değerlendirilmesi hedeflenmiştir. Yöntem: Araştırma metodolojik ve kesitsel tanımlayıcı bir tasarıma sahiptir. Çalışma, Türkiye’de 11 farklı hastanede 18 yaş üstü 1173 hastayı kapsayacak şekilde gerçekleştirilmiştir. Risk değerlendirme aracı literatür taraması ve uzman görüşleri doğrultusunda geliştirilmiş, içerik geçerliği İçerik Geçerlik İndeksi yöntemiyle değerlendirilmiştir. Değerlendiriciler arası güvenirlik Kappa istatistiği ile, iç tutarlılık ise Kuder-Richardson 20 ile ölçülmüştür. Aracın ayırt etme gücü ROC analiziyle değerlendirilmiştir. Araçta yer alan risk faktörleri Dünya Sağlık Örgütü tarafından geliştirilen İşlevsellik, Yetiyitimi ve Sağlığın Uluslararası Sınıflandırılması Modeli’nin grupları doğrultusunda sınıflandırılmıştır. Geliştirilen risk değerlendirme aracı İTAKİ II Düşme Riski Değerlendirme Aracı olarak isimlendirilmiştir. Bulgular: Geliştirilen risk değerlendirme aracında toplam 13 risk faktörü yer almıştır. Aracın İçerik Geçerlik İndeksi .963, ROC eğrisi altında kalan alan değeri .809, değerlendiriciler arası uyum değeri (Kappa) .91, iç tutarlılık katsayısı (Kuder Richardson-20) .568 olarak hesaplanmıştır. Sonuç: Geliştirilen İTAKİ II Düşme Riski Değerlendirme Aracı, düşme riski yüksek hastaları başarılı şekilde ayırt etmektedir. İşlevsellik, Yetiyitimi ve Sağlığın Uluslararası Sınıflandırılması Modeli ile uyumlu yapısı, ulusal ve uluslararası düzeyde veri karşılaştırılabilirliğini artırarak hasta güvenliği uygulamalarına katkı sağlayacaktır.

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Cite This Article

Barış, V., İntepeler, Ş. S., Çelik, İ., Bektaş, M., Engeloğlu, Ş., Kavak, D. G. (2026). Development of the ITAKI II Fall Risk Assessment Scale and Evaluation of Its Psychometric Properties. Etkili Hemşirelik Dergisi, Cilt: 19 Sayı: 3, 383–396. https://doi.org/10.46483/jnef.1650796

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References (37)

  1. Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S., Karthikesalingam, A., King, D.,. & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ digital medicine, 4(1), 65.
  2. Aranda-Gallardo, M., Enriquez de Luna-Rodriguez, M., Vazquez-Blanco, M. J., Canca-Sanchez, J. C., Moya-Suarez, A. B., & Morales-Asencio, J. M. (2017). Diagnostic validity of the STRATIFY and Downton instruments for evaluating the risk of falls by hospitalised acute-care patients: A multicentre longitudinal study. BMC Health Services Research, 17, 277.
  3. Baris, V. K., & Seren Intepeler, S. (2023). Evaluation of the cost‐effectiveness of a multicomponent fall prevention program in hospitalized patients. Nursing & Health Sciences, 25(4), 585-596.
  4. Baris, V. K., & Seren Intepeler, S. (2019). Views of key stakeholders on the causes of patient falls and prevention interventions: A qualitative study using the international classification of functioning, disability and health. Journal of clinical nursing, 28(3-4), 615-628.
  5. Baris, V. K., Intepeler, Ş. S., Ileri, S., & Rastgel, H. (2020). Evaluation of psychometric properties of ITAKI fall risk scale. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi, 13(4), 214-221.
  6. Bhasin, S., Gill, T. M., Reuben, D. B., Latham, N. K., Ganz, D. A., Greene, E. J.,. & Peduzzi, P. (2020). A randomized trial of a multifactorial strategy to prevent serious fall injuries. New England journal of medicine, 383(2), 129-140.
  7. Bujang, M. A., Adnan, T. H. (2016). “Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis”, Journal of Clinical and Diagnostic Research. 10 (10), 01-06.
  8. Cameron ID, Dyer SM, Panagoda CE, et al.: Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst Rev. 2018;9(9): CD005465. 10.1002/14651858.CD005465.pub4.
  9. De Clercq, H., Naudé, A., & Bornman, J. (2021). Older adults’ perspectives on fall risk: Linking results to the ICF. Journal of applied gerontology, 40(3), 328-338.
  10. De Hond, A. A., Steyerberg, E. W., & Van Calster, B. (2022). Interpreting area under the receiver operating characteristic curve. The Lancet Digital Health, 4(12), e853-e855.
  11. DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage. Eckstrom, E., Vincenzo, J. L., Casey, C. M., Gray, S., Cosley, K., Caulley, J.,. & Phelan, E. (2024). American Geriatrics Society response to the world falls guidelines. Journal of the American Geriatrics Society, 72(6), 1669-1686.
  12. Govoni, E., La Porta, F., Negro, A., Valpiani, G., Caselli, S., Bassi, E.,. & Rolli, M. (2022). Evaluation of fall risk factors for among hospitalized patients: systematic review and meta-analysis. European Journal of Public Health, 32(Supplement_3), ckac131-295.
  13. Jehu, D. A., Davis, J. C., Falck, R. S., Bennett, K. J., Tai, D., Souza, M. F.,. & Liu-Ambrose, T. (2021). Risk factors for recurrent falls in older adults: A systematic review with meta-analysis. Maturitas, 144, 23-28.
  14. Johanson, G. A., & Brooks, G. P. (2010). Initial scale development: sample size for pilot studies. Educational and psychological measurement, 70(3), 394-400.
  15. Kisacik, O. G., Cigerci, Y. (2019). Characteristics of Ιnpatient falls in a hospital setting: a retrospective study from Turkey, International Journal of Caring Sciences, 12(2), 768-782.
  16. Klinkenberg, W, D., Potter, P. (2017). Validity of the Johns Hopkins Fall Risk Assessment Tool for Predicting Falls on Inpatient Medicine Services. Journal of Nursing Care Quality, 32(2), 108-113.
  17. Kus, B., Buyukyilmaz F., & Ardic A. (2023). Comparison of three fall risk assessment tools in older hospitalized patients in Turkey: analysis of sensitivity and specificity. Aging clinical and experimental research, 35(5), 1033-1041.
  18. Montero-Odasso, M., Van Der Velde, N., Martin, F. C., Petrovic, M., Tan, M. P., Ryg, J.,. & Masud, T. (2022). World guidelines for falls prevention and management for older adults: a global initiative. Age and ageing, 51(9), afac205.
  19. National Quality Forum. (2011). Serious reportable events in healthcare—2011 update: A consensus report. National Quality Forum. https://www.doh.wa.gov/Portals/1/Documents/2900/NQF2011Update.pdf.
  20. Ong, M. F., Soh, K. L., Saimon, R., Myint, W. W., Pawi, S., & Saidi, H. I. (2023). Falls risk screening tools intended to reduce fall risk among independent community‐dwelling older adults: A systematic review. International journal of nursing practice, 29(4), e13083.
  21. Park, S. H. 2018. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis, Aging Clinical and Experimental Research, 30(1), 1-16.
  22. Polit, D. F., Beck, C. T. 2006. The content validity index: are you sure you know what’s being reported? Critique and recommendations, Research in Nursing & Health, 29(5), 489-497. Ministiry of Health (2020). SKS Hastane (Sürüm 6.1) (1. baskı). Sağlık Bakanlığı Yayınları. ISBN: 978-975-590-766-6.
  23. Soh, S. E., Barker, A. L., Morello, R. T., & Ackerman, I. N. (2020). Applying the International Classification of Functioning, Disability and Health framework to determine the predictors of falls and fractures in people with osteoarthritis or at high risk of developing osteoarthritis: Data from the Osteoarthritis Initiative. BMC musculoskeletal disorders, 21, 1-8.
  24. Strini, V., Schiavolin, R., & Prendin, A. (2021). Fall risk assessment scales: A systematic literature review. Nursing Reports, 11(2), 430-443.
  25. Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Boston, MA: Pearson.
  26. Van Geloven, N., Giardiello, D., Bonneville, E. F., Teece, L., Ramspek, C. L., Van Smeden, M.,. & Steyerberg, E. (2022). Validation of prediction models in the presence of competing risks: a guide through modern methods. bmj, 377.
  27. World Health Organization (WHO). A practical manual for using the International Classification of Functioning, Disability and Health (ICF). Exposure draft for comment. October-2013 https://cdn.who.int/media/docs/default-source/classification/icf/drafticfpracticalmanual2.pdf?sfvrsn=8a214b01_4&download=true.
  28. Xu, Q., Ou, X., & Li, J. (2022). The risk of falls among the aging population: A systematic review and meta-analysis. Frontiers in public health, 10, 902599.
  29. Yilmaz Demir, N., Seren Intepeler, Ş. (2012). Adaptation of morse fall scale to Turkish and determination of sensitivity and specificity. Ege Üniversitesi Hemşirelik Fakültesi Dergisi, 28(1), 57-71.
  30. Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S., Karthikesalingam, A., King, D.,. & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ digital medicine, 4(1), 65.
  31. Bhasin, S., Gill, T. M., Reuben, D. B., Latham, N. K., Ganz, D. A., Greene, E. J.,. & Peduzzi, P. (2020). A randomized trial of a multifactorial strategy to prevent serious fall injuries. New England journal of medicine, 383(2), 129-140.
  32. Cameron ID, Dyer SM, Panagoda CE, et al.: Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst Rev. 2018;9(9): CD005465. 10.1002/14651858.CD005465.pub4.
  33. DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage. Eckstrom, E., Vincenzo, J. L., Casey, C. M., Gray, S., Cosley, K., Caulley, J.,. & Phelan, E. (2024). American Geriatrics Society response to the world falls guidelines. Journal of the American Geriatrics Society, 72(6), 1669-1686.
  34. Govoni, E., La Porta, F., Negro, A., Valpiani, G., Caselli, S., Bassi, E.,. & Rolli, M. (2022). Evaluation of fall risk factors for among hospitalized patients: systematic review and meta-analysis. European Journal of Public Health, 32(Supplement_3), ckac131-295.
  35. Jehu, D. A., Davis, J. C., Falck, R. S., Bennett, K. J., Tai, D., Souza, M. F.,. & Liu-Ambrose, T. (2021). Risk factors for recurrent falls in older adults: A systematic review with meta-analysis. Maturitas, 144, 23-28.
  36. Montero-Odasso, M., Van Der Velde, N., Martin, F. C., Petrovic, M., Tan, M. P., Ryg, J.,. & Masud, T. (2022). World guidelines for falls prevention and management for older adults: a global initiative. Age and ageing, 51(9), afac205.
  37. Van Geloven, N., Giardiello, D., Bonneville, E. F., Teece, L., Ramspek, C. L., Van Smeden, M.,. & Steyerberg, E. (2022). Validation of prediction models in the presence of competing risks: a guide through modern methods. bmj, 377.