Multisite Medical Student–Led Community Culinary Medicine Classes Improve Patients’ Diets: Machine Learning–Augmented Propensity Score–Adjusted Fixed Effects Cohort Analysis of 1381 Subjects

2019 ◽  
pp. 155982761989360 ◽  
Author(s):  
Zachary Stauber ◽  
Alexander C. Razavi ◽  
Leah Sarris ◽  
Timothy S. Harlan ◽  
Dominique J. Monlezun
Keyword(s):  
Machine Learning ◽  
Medical Student ◽  
Fixed Effects ◽  
Dietary Habits ◽  
Vegetable Intake ◽  
Eating Habits ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Hands On ◽  
Culinary Education

Background. Healthy diet represents one of the largest single modifiable risk factors proven to decrease rates of obesity and associated chronic disease, but practical approaches to improving dietary habits through nutritional intervention are limited. Objective. To evaluate the effectiveness of a medical student–led, 6-week culinary course on participants’ dietary knowledge and behaviors, particularly focusing on the tenets of the Mediterranean diet (MedDiet). Design. This study is a prospective multisite cohort study evaluating the effects of a 6-week, hands-on community culinary education course offered at 3 sites. Participants’ knowledge of cooking skills, eating habits, and adherence to the MedDiet were evaluated using a survey prior to beginning and 6 weeks after the completion of the course. Analysis was conducted using multivariable regression to assess subjects’ diets, associated behaviors, and nutrition beliefs according to the number of classes to which they were exposed (0 to >6). Statistical results were then compared with the machine learning results to check statistical validity after selection of the top-performing algorithm from 43 supervised algorithms using 10-fold cross-validation with performance assessed according to accuracy, root relative square error, and root mean square error. Results. Among the 1381 participants, cooking classes significantly improved patients’ overall 9-point MedDiet adherence (β = 0.62, 95% CI 0.23-1.00, P = .002). Participants were more likely to meet MedDiet point requirements for fruit intake (odds ratio [OR] 2.77, 95% CI 1.46-5.23, P = .002), vegetable intake (OR 4.61, 95% CI 1.85-11.53, P = .001), legume intake (OR 2.48, 95% CI 1.45-4.26, P = .001), and olive oil use (OR 2.87, 95% CI 1.44-5.74, P = .003), and were less likely to believe that cooking takes excessive time (OR 0.31, 95% CI 0.16-0.59, P < .001). Conclusion. Hands-on culinary education courses were associated with increased MedDiet adherence and improved knowledge of healthful eating. Such interventions thus represent a cost-effective option for addressing rates of obesity and obesity-related chronic illness.

Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5,847 medical trainees, providers, and patients

2021 ◽  
pp. 1-27
Author(s):  
Dominique J. Monlezun ◽  
Christopher Carr ◽  
Tianhua Niu ◽  
Francesco Nordio ◽  
Nicole DeValle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Nutrition Education ◽  
Meta Analysis ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Number Needed To Treat ◽  
Nutrition Counseling ◽  
Medical Trainees ◽  
Hands On

Abstract Objective: We sought to produce the first meta-analysis (of medical trainee competency improvement in nutrition counseling) informing the first cohort study of patient diet improvement through medical trainees and providers counseling patients on nutrition. Design: (Part A) A systematic review and meta-analysis informing (Part B) the intervention analyzed in the world’s largest prospective multi-center cohort study on hands-on cooking and nutrition education for medical trainees, providers, and patients. Settings: (A) Medical educational institutions. (B) Teaching kitchens. Participants: (A) Medical trainees. (B) Trainees, providers, and patients. Results: (A) Of the 212 citations identified (N=1,698 trainees), 11 studies met inclusion criteria. The overall effect size was 9.80 (95%CI 7.15-12.456.87-13.85; p<0.001), comparable to the machine learning (ML)-augmented results. The number needed to treat for the top performing high quality study was 12. (B) The hands-on cooking and nutrition education curriculum from the top performing study was applied for medical trainees and providers who subsequently taught patients in the same curriculum (N=5,847). The intervention compared to standard medical care and education alone significantly increased the odds of superior diets (high/medium versus low Mediterranean diet adherence) for residents/fellows most (OR 10.79, 95%CI 4.94-23.58; p<0.001) followed by students (OR 9.62, 95%CI 5.92-15.63; p<0.001), providers (OR 5.19, 95%CI 3.23-8.32, p<0.001), and patients (OR 2.48, 95%CI 1.38-4.45; p=0.002), results consistent with those from ML. Conclusions: This study suggests that medical trainees and providers can improve patients’ diets with nutrition counseling in a manner that is clinically and cost effective and may simultaneously advance societal equity.

scholarly journals Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 371
Author(s):  
María Teresa García-Ordás ◽  
Natalia Arias ◽  
Carmen Benavides ◽  
Oscar García-Olalla ◽  
José Alberto Benítez-Andrades
Keyword(s):  
Machine Learning ◽  
Dietary Habits ◽  
Eating Habits ◽  
Mortality Rates ◽  
Machine Learning Techniques ◽  
Infected People ◽  
Learning Techniques ◽  
Average Intake ◽  
Key Points ◽  
Total Average

COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.

scholarly journals Effectiveness of a Canteen Take Away concept in promoting healthy eating patterns among employees

2011 ◽  
Vol 15 (3) ◽  
pp. 452-458 ◽  
Author(s):  
Anne D Lassen ◽  
Lotte Ernst ◽  
Sanne Poulsen ◽  
Klaus K Andersen ◽  
Gitte L Hansen ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Healthy Eating ◽  
Dietary Habits ◽  
Vegetable Intake ◽  
Eating Habits ◽  
Daily Basis ◽  
Dietary Quality ◽  
Average Difference ◽  
Evening Meal ◽  
Meal Intake

AbstractObjectiveTo investigate the effectiveness of a relatively novel concept of providing employees with healthy ready-to-heat meals to bring home to their families, here referred to as Canteen Take Away (CTA).DesignEmployees’ dietary intake on two weekdays when they received free CTA was compared with that on weekdays when they did not receive CTA. Four non-consecutive 24 h dietary recalls were applied to assess dietary intake on a daily basis. Moreover, a digital photographic method was used to assess evening meal intake for three consecutive weeks. Data were analysed using a mixed-effects model.SettingA financial worksite offering CTA.SubjectsTwenty-seven employees.ResultsOverall dietary quality as expressed by the energy density of the food (excluding beverages) was found to be significantly lower on days consuming CTA meals compared to days not consuming CTA with regard to evening meal intake (average difference: −187 (95 % CI −225, −149) kJ/100 g) and on a daily basis (average difference: −77 (95 % CI −132, −21) kJ/100 g). Other favourable differences included increased vegetable intake (average difference: 83 (95 % CI 67, 98) g/evening meal, 109 (95 % CI 62, 155) g/d).ConclusionThe present study shows that providing healthy take-away dinners has potential for promoting healthy dietary habits among employees. This reinforces the importance of availability and convenience as effective tools to promote healthy eating habits.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Yoga in the management of Diabetes Mellitus

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Sreeharsha N. ◽  
Bargale Sushant Sukumar ◽  
Divyasree C. H.
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Eating Habits ◽  
Cost Effective ◽  
The Body ◽  
Positive Thinking ◽  
The World ◽  
Diabetic Population ◽  
Major Factors ◽  
Excess Glucose

Diabetes mellitus is a chronic metabolic disorder in which the body is unable to make proper utilisation of glucose, resulting in the condition of hyperglycaemia. Excess glucose in the blood ultimately results in high levels of glucose being present in the urine (glycosuria). This increase the urine output, which leads to dehydration and increase thirst. India has the largest diabetic population in the world. Changes in eating habits, increasing weight and decreased physical activity are major factors leading to increased incidence of Diabetes. Lifestyle plays an important role in the development of Diabetes. Yoga offers natural and effective remedies without toxic side-effects, and with benefits that extend far beyond the physical. This system of Yoga is a simple, natural programme involving five main principles: proper exercise, proper breathing, proper relaxation, proper diet and positive thinking and meditation. It is a cost effective lifestyle intervention technique.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests

2020 ◽  
Author(s):  
Thomas Tschoellitsch ◽  
Martin Dünser ◽  
Carl Böck ◽  
Karin Schwarzbauer ◽  
Jens Meier
Keyword(s):  
Machine Learning ◽  
Polymerase Chain Reaction ◽  
Characteristic Curve ◽  
Cohort Analysis ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Blood Tests ◽  
Routine Blood ◽  
Machine Learning Model ◽  
Polymerase Chain

Abstract Objective The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2. Methods In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 1353 unique features was trained to predict the RT-PCR results. Results Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1528 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.90. Conclusion Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.

scholarly journals Brown Algae Phlorotannins: A Marine Alternative to Break the Oxidative Stress, Inflammation and Cancer Network

Foods ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1478
Author(s):  
Marcelo D. Catarino ◽  
Sónia J. Amarante ◽  
Nuno Mateus ◽  
Artur M. S. Silva ◽  
Susana M. Cardoso
Keyword(s):  
Oxidative Stress ◽  
Physical Inactivity ◽  
Dietary Habits ◽  
Vegetable Intake ◽  
Fruit And Vegetable Intake ◽  
Bioactive Properties ◽  
Ability To Act ◽  
Pharmaceutical Industries ◽  
Biochemical Mechanisms ◽  
Cancer Network

According to the WHO, cancer was responsible for an estimated 9.6 million deaths in 2018, making it the second global leading cause of death. The main risk factors that lead to the development of this disease include poor behavioral and dietary habits, such as tobacco use, alcohol use and lack of fruit and vegetable intake, or physical inactivity. In turn, it is well known that polyphenols are deeply implicated with the lower rates of cancer in populations that consume high levels of plant derived foods. In this field, phlorotannins have been under the spotlight in recent years since they have shown exceptional bioactive properties, with great interest for application in food and pharmaceutical industries. Among their multiple bioactive properties, phlorotannins have revealed the capacity to interfere with several biochemical mechanisms that regulate oxidative stress, inflammation and tumorigenesis, which are central aspects in the pathogenesis of cancer. This versatility and ability to act either directly or indirectly at different stages and mechanisms of cancer growth make these compounds highly appealing for the development of new therapeutical strategies to address this world scourge. The present manuscript revises relevant studies focusing the effects of phlorotannins to counteract the oxidative stress–inflammation network, emphasizing their potential for application in cancer prevention and/or treatment.

scholarly journals Predictors of COVID-19-Related Perceived Improvements in Dietary Health: Results from a US Cross-Sectional Study

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2097
Author(s):  
Kelly Cosgrove ◽  
Christopher Wharton
Keyword(s):  
Dietary Habits ◽  
Eating Habits ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Lifestyle Changes ◽  
Income Loss ◽  
Cross Sectional ◽  
Healthy Eating Habits ◽  
Health Related ◽  
Survey Responses

The COVID-19 pandemic resulted in substantial lifestyle changes. No US study has identified predictors of perceived dietary healthfulness changes during the pandemic period. This study included analyses of lifestyle and dietary healthfulness changes using 958 survey responses from US primary household food purchasers. Information was collected related to demographics, COVID-19-related household changes, and health-related habits before and during the pandemic. Binary logistic regression identified predictors of perceived increase in dietary healthfulness during the pandemic period. Overall, 59.8%, 16.4%, and 23.4% of participants reported that their eating habits likely changed, may have changed, and likely did not change, respectively. Of the participants whose dietary habits likely or may have changed, 64.1%, 16.8%, and 19% reported healthier, neither healthier nor less healthy, and less healthy eating habits, respectively. COVID-19-related income loss, more meals consumed with household members in front of the television, an increase in food advertisement exposure, increased perceived stress, and better perceived current health were significant predictors of a perceived increase in dietary healthfulness. Overall, dietary habits were perceived to become healthier during the pandemic. The predictors of perceived improvement in dietary healthfulness were surprising and indicate the need for further study of these factors in crisis and noncrisis situations.

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