Applying Machine Learning to Estimate Osteoporosis Risk Based on Compliance with WHO Guidelines for Physical Activity in Postmenopausal Women

2021 ◽  
pp. 98-106
Author(s):  
Horacio Sanchez-Trigo ◽  
Emilio Molina ◽  
Sergio Tejero ◽  
Borja Sañudo
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Postmenopausal Women ◽  
Who Guidelines ◽  
Osteoporosis Risk

Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jae-Geum Shim ◽  
Dong Woo Kim ◽  
Kyoung-Ho Ryu ◽  
Eun-Ah Cho ◽  
Jin-Hee Ahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Postmenopausal Women ◽  
Risk Prediction ◽  
Learning Approaches ◽  
Osteoporosis Risk

scholarly journals Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

2013 ◽  
Vol 54 (6) ◽  
pp. 1321 ◽  
Author(s):  
Tae Keun Yoo ◽  
Sung Kean Kim ◽  
Deok Won Kim ◽  
Joon Yul Choi ◽  
Wan Hyung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Mineral Density ◽  
Postmenopausal Women ◽  
Risk Prediction ◽  
Bone Mineral ◽  
Mineral Density ◽  
Bone Mineral Density Assessment ◽  
Osteoporosis Risk ◽  
Density Assessment

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals 2020 WHO guidelines on physical activity and sedentary behavior

2021 ◽  
Author(s):  
Anthony D. Okely ◽  
Anna Kontsevaya ◽  
Johan Ng ◽  
Chalchisa Abdeta
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Who Guidelines

scholarly journals Effects of a 1-Year Physical Activity Intervention on Markers of Hemostasis among Breast Cancer Survivors: A Randomized Controlled Trial

TH Open ◽  
2021 ◽  
Vol 05 (01) ◽  
pp. e14-e23
Author(s):  
Siv Kjølsrud Bøhn ◽  
Inger Thune ◽  
Vidar Gordon Flote ◽  
Hanne Frydenberg ◽  
Gro Falkenér Bertheussen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Postmenopausal Women ◽  
Cancer Patients ◽  
Physical Activity Intervention ◽  
Intervention Group ◽  
Factor Vii ◽  
Breast Cancer Patients ◽  
Hemostatic Factors

Abstract Introduction Physical activity may reduce the development of breast cancer. Whereas hypercoagulability has been linked to adverse outcomes in breast cancer patients, the effects of physical activity on their hemostatic factors are unknown. The study aimed to assess whether long-term (1 year) physical activity can affect hemostatic factors in breast cancer patients. Methods Fifty-five women (35–75 years) with invasive breast cancer stage I/II were randomized to a physical activity intervention (n = 29) lasting 1 year or to a control group (n = 26), and analyzed as intention to treat. Fibrinogen, factor VII antigen, tissue factor pathway inhibitor, and von Willebrand factor (VWF) antigen as well as prothrombin fragment 1 + 2, the endogenous thrombin potential and D-dimer, were measured in plasma before intervention (baseline), and then after 6 and 12 months. Results Maximal oxygen uptake (measure of cardiorespiratory fitness) decreased the first 6 months among the controls, but remained stable in the intervention group. We found no significant differences between the two study groups regarding any of the hemostatic factors, except a significantly higher increase in factor VII antigen in the intervention group. The effect of the intervention on VWF was, however, significantly affected by menopausal stage, and a significant effect of the intervention was found on VWF among postmenopausal women, even after adjustment for dietary intake. Conclusion Long-term physical activity had no effect on the majority of the hemostatic factors measured, but led to increased plasma concentrations of factor VII antigen and prevented an increase in VWF concentration after breast cancer treatment in postmenopausal women. The clinical impact of these findings for risk of vascular thrombosis warrants further studies.

scholarly journals Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 339
Author(s):  
Alicia Arredondo Eve ◽  
Elif Tunc ◽  
Yu-Jeh Liu ◽  
Saumya Agrawal ◽  
Huriye Erbak Yilmaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Postmenopausal Women ◽  
Diagnostic Test ◽  
Microvascular Disease ◽  
Machine Learning Techniques ◽  
Gas Chromatography Mass Spectrometry ◽  
Circulating Biomarkers ◽  
Diagnostic Biomarkers ◽  
Persistent Symptoms

Coronary microvascular disease (CMD) is a common form of heart disease in postmenopausal women. It is not due to plaque formation but dysfunction of microvessels that feed the heart muscle. The majority of the patients do not receive a proper diagnosis, are discharged prematurely and must go back to the hospital with persistent symptoms. Because of the lack of diagnostic biomarkers, in the current study, we focused on identifying novel circulating biomarkers of CMV that could potentially be used for developing a diagnostic test. We hypothesized that plasma metabolite composition is different for postmenopausal women with no heart disease, CAD, or CMD. A total of 70 postmenopausal women, 26 healthy individuals, 23 individuals with CMD and 21 individuals with CAD were recruited. Their full health screening and tests were completed. Basic cardiac examination, including detailed clinical history, additional disease and prescribed drugs, were noted. Electrocardiograph, transthoracic echocardiography and laboratory analysis were also obtained. Additionally, we performed full metabolite profiling of plasma samples from these individuals using gas chromatography-mass spectrometry (GC–MS) analysis, identified and classified circulating biomarkers using machine learning approaches. Stearic acid and ornithine levels were significantly higher in postmenopausal women with CMD. In contrast, valine levels were higher for women with CAD. Our research identified potential circulating plasma biomarkers of this debilitating heart disease in postmenopausal women, which will have a clinical impact on diagnostic test design in the future.

scholarly journals Retraction Note: Associations between VDR Gene Polymorphisms and Osteoporosis Risk and Bone Mineral Density in Postmenopausal Women: A systematic review and Meta-Analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liang Zhang ◽  
Xin Yin ◽  
Jingcheng Wang ◽  
Daolinag Xu ◽  
Yongxiang Wang ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Systematic Review ◽  
Postmenopausal Women ◽  
Bone Mineral ◽  
Gene Polymorphisms ◽  
Meta Analysis ◽  
Mineral Density ◽  
Vdr Gene ◽  
Vdr Gene Polymorphisms ◽  
Osteoporosis Risk

Editor's Note: this Article has been retracted; the Retraction Note is available at https://doi.org/10.1038/s41598-021-88654-1.

scholarly journals Combined Effects of Unhealthy Lifestyle Behaviors on Metabolic Syndrome among Postmenopausal Women

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 848
Author(s):  
Jin-Suk Ra ◽  
Hyesun Kim
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Postmenopausal Women ◽  
Abdominal Obesity ◽  
Impaired Fasting Glucose ◽  
Fasting Glucose ◽  
Dietary Behavior ◽  
Lifestyle Behaviors ◽  
Unhealthy Lifestyle ◽  
Insufficient Physical Activity

This study aimed to identify the combined effects of unhealthy lifestyle behaviors, including diet, sedentary behavior, and physical activity on metabolic syndrome (MS) and components of MS among postmenopausal women. Secondary data analysis was conducted using the Korean National Health and Nutrition Examination Survey (2014–2018) with a cross-sectional study design. Logistic regression analysis was conducted with data from 6114 Korean postmenopausal women. While no significant effects of unhealthy lifestyle behaviors, either individually or as a combination, were found for MS, prolonged sedentary behavior without poor dietary behavior and insufficient physical activity was associated with increased likelihood of abdominal obesity (adjusted odds ratio [AOR]: 1.59, 95% confidence interval [CI]: 1.10–2.29) and impaired fasting glucose (AOR: 1.54, 95% CI: 1.13–2.10). The combination of poor dietary behavior and prolonged sedentary behaviors was also associated with increased likelihood of abdominal obesity (AOR: 1.48, 95% CI: 1.10–2.00) and impaired fasting glucose (AOR: 1.49, 95% CI: 1.14–1.96). In addition, prolonged sedentary behavior and insufficient physical activity together were associated with increased likelihood of abdominal obesity (AOR: 2.81, 95% CI: 1.90–4.20) and impaired fasting glucose (AOR: 1.59, 95% CI: 1.13–2.24). Finally, combining poor dietary behavior, prolonged sedentary behavior, and insufficient physical activity was also associated with increased likelihood of abdominal obesity (AOR: 2.05, 95% CI: 1.50–2.80) and impaired fasting glucose (AOR: 1.71, 95% CI: 1.32–2.23). Strategies for replacing sedentary behavior of postmenopausal women with activities are warranted for prevention of abdominal obesity and impaired fasting glucose.

scholarly journals Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools

Nutrients ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1681 ◽  
Author(s):  
Ramyaa Ramyaa ◽  
Omid Hosseini ◽  
Giri P. Krishnan ◽  
Sridevi Krishnan
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Body Weight ◽  
Energy Balance ◽  
Numerical Data ◽  
Support Vector ◽  
Learning Tools ◽  
K Nearest Neighbor ◽  
Numerical Predictions

Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters. From the Women’s Health Initiative Observational Study (WHI OS), carbohydrates, proteins, fats, fibers, sugars, and physical activity variables, namely energy expended from mild, moderate, and vigorous intensity activity, were used to predict current body weight (both as body weight in kilograms and as a body mass index (BMI) category). Several machine learning tools were used for this prediction. Finally, cluster analysis was used to identify putative phenotypes. For the numerical predictions, the support vector machine (SVM), neural network, and k-nearest neighbor (kNN) algorithms performed modestly, with mean approximate errors (MAEs) of 6.70 kg, 6.98 kg, and 6.90 kg, respectively. For categorical prediction, SVM performed the best (54.5% accuracy), followed closely by the bagged tree ensemble and kNN algorithms. K-means cluster analysis improved prediction using numerical data, identified 10 clusters suggestive of phenotypes, with a minimum MAE of ~1.1 kg. A classifier was used to phenotype subjects into the identified clusters, with MAEs <5 kg for 15% of the test set (n = ~2000). This study highlights the challenges, limitations, and successes in using machine learning tools on self-reported data to identify determinants of energy balance.

Feelings Of Energy Are Associated With Physical Activity And Sleep Quality, But Not Adiposity, In Middle-aged Postmenopausal Women

2014 ◽  
Vol 46 ◽  
pp. 684
Author(s):  
Christie L. Ward-Ritacco ◽  
Amanda L. Adrian ◽  
Patrick J. O’Connor ◽  
Mary Ann Johnson ◽  
Laura Q. Rogers ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Postmenopausal Women ◽  
Middle Aged
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