47 research outputs found
The health effects of medical nutrition therapy by dietitians in patients with diabetes: A systematic review and meta-analysis
No full textAims: Intensive lifestyle, dietary interventions and patient education have been recommended as key milestones in to facilitate the management of Diabetes and contain the growing incidence. We performed a systematic review and meta-analysis to assess the health benefits of medical nutrition therapy among patients with diabetes. Design: A systematic search was performed in MEDLINE/PubMed, SCOPUS, and Cochrane library from onset up to February 2019 to identify trials investigating the health effect of Medical nutrition (MNT) in patients with diabetes. Random-effects models were used to calculate the effect sizes as weighted mean difference (WMD) and 95% confidence intervals (CI). Results: Eleven studies containing 1227 participants were included in the meta-analysis. Pooled results showed a significant reduction in Fasting blood sugar (FBS) (WMD= −8.85 mg/dl, 95% CI: −14.41, −3.28), HbA1c (WMD: −0.43%, 95% CI: −0.69, −0.17), weight (WMD: −1.54 kg, 95% CI: −2.44, −0.64), Body mass index (BMI) (WMD: −0.34 Kg/m2, 95% CI: −0.52, −0.17), waist circumference (WMD: −2.16 cm, 95% CI: −4.09, −0.23), cholesterol (WMD: −4.06 mg/dl, 95% CI: −7.31, −0.81), Systolic blood pressure (SBP) (WMD: −7.90 mmHg, 95% CI: −13.03, −2.77). Results of meta-regression analysis based on age of participants and duration of intervention were not significant. Conclusions: Patients with diabetes who received medical nutrition therapy showed significant improvements in outcome measures of FBS, HbA1c, weight, BMI, waist circumference, cholesterol, and SBP.</p
A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.
No full textWith the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard
Factors Affecting The Saudi Gen-Z To Adopt The Metaverse Workplace : Application Of Utaut2
No full textThe metaverse concept gained traction, notably in 2021, when Facebook changed its name to Meta
and Nvidia's CEO stated that the company's next step would be establishing a metaverse. However,
the historical context is rarely presented; in 1992, American author Neal Stephenson's science
fiction novel Snow Crash introduced the metaverse concept for the first time. In Snow Crash, the
protagonists morph into avatars and function in the metaverse, a three-dimensional (3D) virtual
Reality. This notion evolved and took on several shapes. Augmented Reality, Virtual Reality,
lifelogging, and Mirror World users have significantly impacted an individual level and society in
various fields such as education, health, and the workplace. This research paper is determined to
measure the potential of gen z in the Kingdom of Saudi Arabia to adopt the concept of the
"metaverse workplace" by developing a thorough grasp of the factors that influence the Saudi Z -
generation's attraction to the digital realm of the metaverse as a new career prospect for future
growth. Using Google Forms, a quantitative questionnaire with 18 items was employed as the data
collection strategy for the methodology. Range from short answer questions to ranking questions
to multiple choice questions. to the intended audience. Which are "Gen Z," who are now enrolled
in universities. The number of respondents are 250 and the results show that performance and
facilitating conditioning are the most significant elements to the process of adopting the metaverse
work place . Due to the paucity of research on this topic in Saudi Arabia, this study has added to
the body of knowledge in this area
The effect of Brazil nuts on selenium levels, Glutathione peroxidase, and thyroid hormones:A systematic review and meta-analysis of randomized controlled trials
No full textBrazil nuts or Bertholletia excelsa provide a rich natural source of magnesium, phosphorus, and manganese. Furthermore, it is rich of anti-oxidants such as selenium, vitamin E, and phenols like gallic acid and ellagic acid and have improvement effects on plasma selenium levels, Glutathione peroxidase (GPx), and thyroid hormones but the results have not been summarized in a meta-analysis. The purpose of this study is to investigate the effect of brazil nut on plasma selenium levels, GPx, and thyroid hormones. Literature search was done in MEDLINE/PubMed, Scopus and web of sciences databases up to October 2019. Studies included that had RCTs design, use brazil nut as intervention, and reported selenium levels, Glutathione peroxidase, or thyroid hormones as outcome. PRISMA guidelines followed to perform this meta-analysis and results combined using DerSimonian and Laird random effect model. Seven studies with 315 participant's included and analyzed in this meta-analysis. Mean duration of intervention was 11 weeks and mean dosage of brazil nut was 9.42 g/day in included studies. Our study found brazil nut have significant increasing effect on plasma selenium levels (WMD: 87.63 microg/l, 95% CI: 36.02, 139.24, I 2 = 98%). Furthermore, Brazil nut had increasing effect on GPx levels too (WMD: 8.05U/gHb, 95% CI:0.65, 15.45, I 2 = 96%) but brazil nut had no significant effect on T3 (WMD: 0.06 pg/ml, 95% CI: −0.50, 0.39, I 2 = 74%), T4 (WMD: −0.01 pg/ml, 95% CI: −0.46, 0.44, I 2 = 82%), and TSH levels (WMD: 0.01 ng/ml, 95% CI: −0.03, 0.05, I2 = 0.67%).The findings of this meta-analysis indicates brazil nut increase plasma selenium and GPx levels. </p
The efficacy and safety of lecanemab 10 mg/kg biweekly compared to a placebo in patients with Alzheimer’s disease: a systematic review and meta-analysis of randomized controlled trials
No full textAlzheimer’s disease, prevalent in individuals aged 60 and above, constitutes most dementia cases and significantly impairs memory and cognitive functions. With global Alzheimer’s cases projected to triple by 2050, there is a pressing need for effective interventions. Lecanemab, a monoclonal antibody targeting amyloid-beta plaques, shows promise in slowing Alzheimer’s progression. Positive clinical trial results have instilled hope in patients, prompting ongoing research to advance understanding and intervention possibilities. To contribute to this knowledge base, we conducted a systematic review and meta-analysis, focusing on lecanemab’s efficacy and safety at a dosage of 10 mg/kg. This comprehensive approach aimed to address gaps in the current literature, scrutinize research disparities, and guide future investigations. Applying strict inclusion/exclusion criteria, we assessed study details, participant information, and intervention specifics, using the Cochrane risk of bias tool for quality evaluation. Statistical analyses, conducted with R software, included risk ratios and mean differences, assessing heterogeneity and publication bias. The meta-analysis reveals a significant positive effect of lecanemab (10 mg/kg biweekly) on cognitive outcomes in Alzheimer’s disease. Consistent reductions in ADCOMS, CDR-SB, and ADAS-cog14 scores across studies indicate drug efficacy with narrow confidence intervals and no significant heterogeneity. While TEAE shows no significant difference, heightened risks of ARIA-E and ARIA-H associated with lecanemab underscore the need for vigilant safety monitoring in clinical practice. Despite the drug efficacy, the study emphasizes a balanced assessment of benefits and potential risks associated with lecanemab, providing critical insights for clinicians evaluating its use in addressing cognitive impairment in individuals with Alzheimer’s disease
Items and definitions for data extraction.
No full textWith the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models’ development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer’s disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.</div
Number of studies for publication years.
No full textWith the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models’ development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer’s disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.</div
Overview of the included studies with the type of ML prediction models development without conducting external validation (n = 81).
No full textOverview of the included studies with the type of ML prediction models development without conducting external validation (n = 81).</p
Percentage presentation of the results of (PROBAST) tool.
No full textThe tool has two components. Component 1. Risk of bias (4 domains: Participants, predictors, outcome, and analysis). Component 2. Concern of applicability (3 domains: Participants, predictors, and outcome).</p
The effect of almonds consumption on Blood pressure: A systematic review and dose-response meta-analysis of randomized control trials
No full textAlmond is rich in antioxidants and phytochemicals such as methylquercetin, protocatechuic acid, catechin, flavonoids, p-hydroxybenzoic acid, resveratrol, vanillic acid, and kaempferol. The aim of the present study was to systematically review and dose-response meta-analyses the effects of almond consumption on systolic and diastolic blood pressure (SBP/DBP), respectively, in Randomized Controlled Trials (RCTs). A systematic search was performed in PubMed/MEDLINE, web of sciences and SCOPUS by 2 researchers, independently to identify randomised controlled trials up to July 2019. There were no time or language restrictions. PRISMA guidelines were followed in conducting this meta-analysis. Fifteen studies with 21 arms, containing 853 participants, reported SBP as an outcome measure. Pooled results showed significant reduction in SBP (WMD: -0.90 mmHg, 95% CI: -1.74, -0.06, P heterogeneity=0.94) by almond intervention. There is no significant effect from almond consumption on DBP (WMD: 0.67 mmHg, 95% CI: -1.93, 0.60, P heterogeneity=0.001). Meta-regression analysis showed dose of used almond (g/d) as source of heterogeneity between results of DBP. In conclusion results of this meta-analysis showed reduce effect of Almonds on systolic blood pressure
