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The performance of an online osteoporosis detection system a sensitivity and specificity analysis

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  • Shu Fang Chang
  • Chin Ming Hong
  • Rong Sen Yang

Abstract

Aims and objectives To develop an online system for the detection of osteoporosis risk and to test its accuracy. Background Osteoporosis is a silent killer; usually, there are no symptoms, such as pain, until bone erosion and fracture occur. The risks of osteoporosis have been underestimated and neglected; as a result, osteoporosis can be as dangerous as heart diseases and cancers that lead to a healthcare crisis. Design Cross‐sectional study. Methods The study participants were individuals presenting for routine health examinations at a medical centre in Taiwan from 2006–2007. Women over 30 years of age who underwent dual‐energy X‐ray absorptiometry scanning for measurement of bone mineral density were eligible for this study. The system for osteoporosis detection and health risk, which was developed in this study, was analysed. Results The findings indicated a high sensitivity of 75%, specificity of 75%, positive predictive value of 75% and negative predictive value of 75%. In addition, the online osteoporosis detective system had a higher predictive power (24·2% vs. 11%) and a similar cut‐off point (33% vs. 27%) compared with the tool designed by the International Osteoporosis Foundation. Conclusion The online system for detection of osteoporosis risk had excellent reliability and validity. It performed well in predicting osteoporosis and the cut‐off point used for identifying the risk among women at risk of developing osteoporosis. Therefore, it is suitable for the Asian women and can help women achieve the goals of early detection and health promotion. Relevance to clinical practice Early detection is the only way to prevent osteoporosis. Professional nurses should apply effective technology to promote health care in community‐dwelling people.

Suggested Citation

  • Shu Fang Chang & Chin Ming Hong & Rong Sen Yang, 2014. "The performance of an online osteoporosis detection system a sensitivity and specificity analysis," Journal of Clinical Nursing, John Wiley & Sons, vol. 23(13-14), pages 1803-1809, July.
    • Handle: RePEc:wly:jocnur:v:23:y:2014:i:13-14:p:1803-1809
    DOI: 10.1111/jocn.12209
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    1. Yang, Nan-Ping & Deng, Chung-Yeh & Chou, Yiing-Jenq & Chen, Po-Quang & Lin, Ching-Heng & Chou, Pesus & Chang, Hong-Jen, 2006. "Estimated prevalence of osteoporosis from a Nationwide Health Insurance database in Taiwan," Health Policy, Elsevier, vol. 75(3), pages 329-337, February.
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    Cited by:

    1. Lan-Ping Lin & Wei-Ju Lai & Shang-Wei Hsu & Jin-Ding Lin, 2020. "Early Osteoporosis Risks and Associated Factors among Caregivers Working in Disability Institutions: IOF One-Minute Osteoporosis Risk Check," IJERPH, MDPI, vol. 17(9), pages 1-10, May.

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