Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis (Preprint)

Akinosun, Adewale Samuel; Polson, Rob; Diaz-Skeete, Yohanca; Kock, Johannes H. De; Carragher, Lucia; Leslie, Stephen J; Grindle, Mark; Gorely, Trish

doi:10.2196/preprints.21061

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Cardiac disease is the major cause of death all over the world, with 17.9 million deaths annually, as per the World Health Organization reports [1].Due to unawareness and some unhealthy activities, the causes of the increase in the risk of heart diseases like diabetes, obesity, hypertension etc. [2]. Heart disease can be predicted through many dysfunctions, like Systolic Dysfunction and Diastolic Dysfunction [3].…”

Section: Introductionmentioning

confidence: 99%

Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach

Anjum

Riaz

Latif

2022

IJIST

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Cardiac disease is the major cause of deaths all over the world, with 17.9 million deaths annually, as per World Health Organization reports. The purpose of this study is to enable a cardiologist to early predict the patient’s condition before performing the echocardiography test. This study aims to find out whether diastolic function or diastolic dysfunction using symptoms through machine learning. We used the unexplored dataset of diastolic dysfunction disease in this study and checked the symptoms with cardiologist to be enough to predict the disease. For this study, the records of 1285 patients were used, out of which 524 patients had diastolic function and the other 761 patients had diastolic dysfunction. The input parameters considered in this detection include patient age, gender, BP systolic, BP diastolic, BSA, BMI, hypertension, obesity, and Shortness of Breath (SOB). Various machine learning algorithms were used for this detection including Random Forest, J.48, Logistic Regression, and Support Vector Machine algorithms. As a result, with an accuracy of 85.45%, Logistic Regression provided promising results and proved efficient for early prediction of cardiac disease. Other algorithms had an accuracy as follow, J.48 (85.21%), Random Forest (84.94%), and SVM (84.94%). Using a machine learning tool and a patient’s dataset of diastolic dysfunction, we can declare either a patient has cardiac disease or not.

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Section: Introductionmentioning

confidence: 99%

Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach

Anjum

Riaz

Latif

2022

IJIST

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The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials

Seid,

Fufa,

Bitew

2024

Front. Digit. Health

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IntroductionDigital tools, such as mobile apps and the Internet, are being increasingly used to promote healthy eating habits. However, there has been inconsistent reporting on the effectiveness of smartphones and web-based apps in influencing dietary behaviors. Moreover, previous reviews have been limited in scope, either by focusing on a specific population group or by being outdated. Therefore, the purpose of this review is to investigate the impacts of smartphone- and web-based dietary interventions on promoting healthy eating behaviors worldwide.MethodsA systematic literature search of randomized controlled trials was conducted using databases such as Google Scholar, PubMed, Global Health, Informit, Web of Science, and CINAHL (EBSCO). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to prepare the entire document. EndNote (version 20) was used for reference management. The risk of bias in the articles was assessed using the “Revised Cochrane Risk of Bias tool for randomized trials (RoB 2.0)” by the Cochrane Collaboration. Narrative synthesis, using text and tables, was used to present the results. The study was registered in PROSPERO under protocol number CRD42023464315.ResultsThis review analyzed a total of 39 articles, which consisted of 25 smartphone-based apps and 14 web-based apps. The studies involved a total of 14,966 participants. Out of the 25 studies, 13 (52%) showed that offline-capable smartphone apps are successful in promoting healthier eating habits. The impact of smartphone apps on healthy adults has been inconsistently reported. However, studies have shown their effectiveness in chronically ill patients. Likewise, internet-based mobile apps, such as social media or nutrition-specific apps, have been found to effectively promote healthy eating behaviors. These findings were consistent across 14 studies, which included healthy adults, overweight or obese adults, chronically ill patients, and pregnant mothers.ConclusionOverall, the findings suggest that smartphone apps contribute to improving healthy eating behaviors. Both nutrition-specific and social media-based mobile apps consistently prove effective in promoting long-term healthy eating habits. Therefore, policymakers in the food system should consider harnessing the potential of internet-based mobile apps and social media platforms to foster sustainable healthy eating behaviors.

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Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis (Preprint)

Cited by 2 publications

References 22 publications

Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach

Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach

The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials

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