2019
DOI: 10.1177/2055207619844362
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Personalised measures of obesity using waist to height ratios from an Australian health screening program

Abstract: Objectives The aim of the current study is to generate waist circumference to height ratio cut-off values for obesity categories from a model of the relationship between body mass index and waist circumference to height ratio. We compare the waist circumference to height ratio discovered in this way with cut-off values currently prevalent in practice that were originally derived using pragmatic criteria. Method Personalized data including age, gender, height, weight, wa… Show more

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Cited by 3 publications
(6 citation statements)
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References 22 publications
(52 reference statements)
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“…In line with the focus of a dose‐ranging and efficacy analysis (with minimal comorbidity intervention), a rigorous inclusion and exclusion criteria were utilized to ensure a stringent selection process in accordance with the specifications outlined in the research protocol. This step calls for the recruitment of obese participants defined by a combination of parameters to prevent bias, namely (a) WC >60% of height, (b) BMI, and (c) BW with or without comorbidities who were deemed medically healthy (treated or untreated) 20 . The study participants were selected from six (four primary care clinics and two tertiary care hospitals) clinical sites between January 2020 and April 2021.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In line with the focus of a dose‐ranging and efficacy analysis (with minimal comorbidity intervention), a rigorous inclusion and exclusion criteria were utilized to ensure a stringent selection process in accordance with the specifications outlined in the research protocol. This step calls for the recruitment of obese participants defined by a combination of parameters to prevent bias, namely (a) WC >60% of height, (b) BMI, and (c) BW with or without comorbidities who were deemed medically healthy (treated or untreated) 20 . The study participants were selected from six (four primary care clinics and two tertiary care hospitals) clinical sites between January 2020 and April 2021.…”
Section: Methodsmentioning
confidence: 99%
“…This step calls for the recruitment of obese participants defined by a combination of parameters to prevent bias, namely (a) WC >60% of height, (b) BMI, and (c) BW with or without comorbidities who were deemed medically healthy (treated or untreated). 20 The study participants were selected from six (four primary care clinics and two tertiary care hospitals) clinical sites between January 2020 and April 2021.…”
Section: Participants and Materialsmentioning
confidence: 99%
“…Detecting patterns from high volumes of health data for assessing health risks and disease requires an appropriate big data infrastructure. Such an architecture should support big data analytics where health data gets processed intelligently deriving meaningful health patterns thereby capable of providing useful insight for improving personalized healthcare [21], [22]. Many key aspects of big data such as managing the 5 V's (volume, variety, velocity, veracity, and value) for healthcare applications pose a major challenge [13].…”
Section: Int J Elec and Comp Eng Issn: 2088-8708mentioning
confidence: 99%
“…There are similarities among these classifiers where each algorithm's prediction is achieved by the top-ranking attribute or having the highest weight or probability. A review of literature shows capabilities ❒ ISSN: 2088-8708 of big data analytics through several studies comparing the performance of different variants of such supervised machine learning algorithms for disease prediction [21], [59]. In summary, with our proposed framework we postulate an adaptive use of the above mentioned machine learning algorithms for performing big data analytics of health data to enhance personalized healthcare.…”
Section: Evaluation and Limitationsmentioning
confidence: 99%
“…• Improving personalised healthcare with intelligent therapeutic decision support. With data-driven point of care diagnostic, personalized healthcare using intelligent health data analytics of big data can lead to discovery of interacting variables and hidden trends for a better therapeutic decision support (Abidi & Abidi, 2019;Jelinek et al, 2019;Stranieri et al, 2016).…”
Section: • Prevalence Of Chronic Conditions Requires the Detection Of...mentioning
confidence: 99%