Digital health in osteoporosis management: A literature review
Main Article Content
Abstract
Osteoporosis is a progressive silent disease. The incidence and mortality of osteoporotic fractures are increasing rapidly, leading to an increased burden on patients, families, and society. The objective of this study is to explore the applications of digital health in the screening, monitoring and treatment of osteoporosis. The study was conducted according to a comprehensive research process, searching the Pubmed database. The search content focused on three main parts: osteoporosis, digital health, and effectiveness. The selected original articles were published from January 2014 to September 2024 in peer-reviewed international journals. A total of 18 articles were included in the analysis. The main applications of digital health include: predicting osteoporosis (38.9%) and monitoring the treatment of osteoporosis (62.1%). Most studies have shown positive results in improving patients’ health.
Article Details
Keywords
Osteoporosis, fracture, machine learning, digital health, outcomes, effective
References
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9. Ou Yang W Y, Lai C C, Tsou M T, et al. Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data. International journal of environmental research and public health. 2021; 18(14).
10. E Erjiang, Wang Tingyan, Yang Lan, et al. Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study. Journal of Clinical Densitometry. 2021; 24(4): 527-537.
11. Lee Chaewon, Joo Gihun, Shin Seunghun, et al. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Scientific reports. 2023; 13(1): 21800.
12. Ravn Jakobsen P, Hermann A P, Søndergaard J, et al. Help at hand: Women’s experiences of using a mobile health application upon diagnosis of asymptomatic osteoporosis. SAGE open medicine. 2018; 6:2050312118807617.
13. Ryan P, Brown R L, Csuka M E, et al. Efficacy of Osteoporosis Prevention Smartphone App. Nursing research. 2020; 69(1): 31-41.
14. Li C T, Hung G K, Fong K N, et al. Effects of home-based occupational therapy telerehabilitation via smartphone for outpatients after hip fracture surgery: A feasibility randomised controlled study. Journal of telemedicine and telecare. 2022; 28(4): 239-247.
15. Park Y J, Lee S J, Shin N M, et al. Application and Effect of Mobiletype-Bone Health Intervention in Korean Young Adult Women with Low Bone Mass: A Randomized Control Trial. Asian nursing research. 2017; 11(1): 56-64.
16. Nguyen D T, Ho-Le TP, Pham L, et al. BONEcheck: A digital tool for personalized bone health assessment. Osteoporosis and sarcopenia. 2023; 9(3): 79-87.
17. Papaioannou A, McCloskey E, Bell A, et al. Use of an electronic medical record dashboard to identify gaps in osteoporosis care. Archives of osteoporosis. 2021; 16(1): 76.
18. Lu K, Wu Y M, Shi Q, et al. A novel fracture liaison service using digital health: impact on mortality in hospitalized elderly osteoporotic fracture patients. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2024; 35(1): 53-67.
19. Lindsay J R, Lawrenson G, English S, et al. A service evaluation of e-triage in the osteoporosis outpatient clinic-an effective tool to improve patient access? Archives of osteoporosis. 2020; 15(1): 53.
20. Ye C, Li J, Hao S, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International journal of medical informatics. 2020; 137: 104105.
2. Odén A, McCloskey E V, Kanis J A, et al. Burden of high fracture probability worldwide: secular increases 2010-2040. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2015; 26(9): 2243-2248.
3. Chandran M, Ganesan G, Tan K B, et al. Cost-effectiveness of FRAX®-based intervention thresholds for management of osteoporosis in Singaporean women. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2021; 32(1): 133-144.
4. Kim S W, Won Y J, Chae D S, et al. A New Fracture Liaison Service Using the Mobile Application and IoT Sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2019; 2019: 3486-3489.
5. Kwon Y, Lee J, Park J H, et al. Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women. Healthcare (Basel, Switzerland). 2022; 10(6).
6. Yang Q, Cheng H, Qin J, et al. A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study. JMIR aging. 2023; 6:e46791.
7. Bui H M, Ha M H, Pham H G, et al. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Scientific reports. 2022; 12(1): 20160.
8. Chang Ching-Yao, Peng Chung-Hsin, Chen Fang-Yu, et al. The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up. Sci Rep, 2023; 14(1): 23234.
9. Ou Yang W Y, Lai C C, Tsou M T, et al. Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data. International journal of environmental research and public health. 2021; 18(14).
10. E Erjiang, Wang Tingyan, Yang Lan, et al. Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study. Journal of Clinical Densitometry. 2021; 24(4): 527-537.
11. Lee Chaewon, Joo Gihun, Shin Seunghun, et al. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Scientific reports. 2023; 13(1): 21800.
12. Ravn Jakobsen P, Hermann A P, Søndergaard J, et al. Help at hand: Women’s experiences of using a mobile health application upon diagnosis of asymptomatic osteoporosis. SAGE open medicine. 2018; 6:2050312118807617.
13. Ryan P, Brown R L, Csuka M E, et al. Efficacy of Osteoporosis Prevention Smartphone App. Nursing research. 2020; 69(1): 31-41.
14. Li C T, Hung G K, Fong K N, et al. Effects of home-based occupational therapy telerehabilitation via smartphone for outpatients after hip fracture surgery: A feasibility randomised controlled study. Journal of telemedicine and telecare. 2022; 28(4): 239-247.
15. Park Y J, Lee S J, Shin N M, et al. Application and Effect of Mobiletype-Bone Health Intervention in Korean Young Adult Women with Low Bone Mass: A Randomized Control Trial. Asian nursing research. 2017; 11(1): 56-64.
16. Nguyen D T, Ho-Le TP, Pham L, et al. BONEcheck: A digital tool for personalized bone health assessment. Osteoporosis and sarcopenia. 2023; 9(3): 79-87.
17. Papaioannou A, McCloskey E, Bell A, et al. Use of an electronic medical record dashboard to identify gaps in osteoporosis care. Archives of osteoporosis. 2021; 16(1): 76.
18. Lu K, Wu Y M, Shi Q, et al. A novel fracture liaison service using digital health: impact on mortality in hospitalized elderly osteoporotic fracture patients. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2024; 35(1): 53-67.
19. Lindsay J R, Lawrenson G, English S, et al. A service evaluation of e-triage in the osteoporosis outpatient clinic-an effective tool to improve patient access? Archives of osteoporosis. 2020; 15(1): 53.
20. Ye C, Li J, Hao S, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International journal of medical informatics. 2020; 137: 104105.