Translation and Validation of American Diabetes Association Diabetes Risk Test: The Malay Version

Fauzi, Nurul Fatihah Mohd; Wafa, Sharifah Wajihah; Abdullah, Mohd Ibrahim; Raj, Naresh Bhaskar; Hassan, Nurulhuda Mat

doi:10.21315/mjms2022.29.1.11

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…Google Forms will be used due to its advantages: easy to build the questionnaire, has unlimited surveys, and is free. ADA diabetes risk test was translated and validated into the Malay language by two experts in the fields of nutrition and dietetics, as well as 35 target users (Nurul Fatihah et al, 2022). The risk score is based on a set of variables not requiring laboratory tests that are used as a tool to predict the risk of T2DM or identify undetected T2DM; these variables are age, gender, and family history of diabetes, history of gestational diabetes in women, history of hypertension, physical activity, and BMI.…”

Section: Recruitmentmentioning

confidence: 99%

Diabetes prevention through digital therapy for highrisk individuals: Study protocol for the Malaysia Diabetes Prevention Programme (MyDiPP)

Fauzi¹,

Wafa²,

Raj³

et al. 2023

Mal J Nutr

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Introduction: Intervention approaches that integrate human coaching into technology are considered as a convenient, accessible and scalable method to reach a larger population at risk. The objective of this paper is to present the protocol for a randomised controlled trial that evaluates the efficacy of a lifestyle intervention programme via a mobile phone app (MyDiPP), which aims to prevent diabetes among adults at risk of developing diabetes. Methods: MyDiPP intervention is to be delivered for 12 months with multiple approaches (weight loss, dietary modification, physical activity, and quality of life). Eligible adults aged 18-65 years, overweight/obese (body mass index, BMI ≥ 23kg/m2), and at high risk of type 2 diabetes [American Diabetes Association (ADA) Diabetes Risk Score ≥5, or haemoglobin A1c (HbA1c) of 5.6-6.2%], will be randomly assigned to one of two study groups (intervention or usual care control groups) in a 1:1 ratio using simple randomisation. Results: Changes in weight and HbA1c level (primary outcomes), and changes in physical activity level, dietary intake, and quality of life (secondary outcomes) will be assessed at 6 and 12 months. Conclusion: This study protocol describes the first digital therapy for diabetes prevention in Malaysia, which will determine whether the effect of this intervention is larger than the effect of usual care in reducing body weight and HbA1c level, and improving dietary intake, physical activity, and quality of life of high-risk individuals. Results from this trial may be useful for preventing type 2 diabetes mellitus in Malaysia.

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Section: Recruitmentmentioning

confidence: 99%

Diabetes prevention through digital therapy for highrisk individuals: Study protocol for the Malaysia Diabetes Prevention Programme (MyDiPP)

Fauzi¹,

Wafa²,

Raj³

et al. 2023

Mal J Nutr

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show abstract

“…Framingham cardiovascular risk score) derived from the Cox proportional-hazards model. While some studies 64,68,69 have shown that adjustments based on sex or other local factors can significantly improve risk prediction, adjusting such scores for different populations may be difficult, resulting in unaccounted heuristics or biases 70 . In this work, we present a flexible data-driven definition of health risk from a single lab visit.…”

Section: Association Of Lifestyle Factors With Blood Test Reference I...mentioning

confidence: 99%

Novel Representation Learning Improves Personalizing Blood Test Ranges and Disease Risk Prediction

Heydari

Prieto

Patel

et al. 2023

Preprint

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Blood tests are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. however, quantitative approaches for personalizing such metrics are nascent and often ignore important factors such as health behaviors. Moreover, recent studies have shown that raw (untransformed) representations of health records are inadequate for constructing predictive models, especially when considering a single time point. Here, we investigate the association of activity and sleep with blood biomarker ranges, and based on our results, propose Proteus, a new deep metric learning algorithm that accounts for health behaviors. We show that Proteus significantly improves the performance of several downstream analyses, including the prediction of future health risk in currently-healthy patients using a single laboratory visit. Building upon our findings, we additionally introduce DeepRange, a novel lifestyle-informed algorithm, which utilizes deep-learned embeddings for estimating personalized optimal blood test ranges. Our proposed methodology for personalized blood biomarker ranges and single-visit health risk prediction can be readily implemented and has the potential to significantly improve health outcomes by enabling early intervention and enhanced personalization.

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A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring

Barua,

Glantz,

Larez

et al. 2024

Sci Rep

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In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37–63%)] compared to pre-T2D [36% (95% CI 31–48%), p = 0.01] and at-risk participants [34% (95% CI 27–39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

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Translation and Validation of American Diabetes Association Diabetes Risk Test: The Malay Version

Cited by 6 publications

References 51 publications

Diabetes prevention through digital therapy for highrisk individuals: Study protocol for the Malaysia Diabetes Prevention Programme (MyDiPP)

Diabetes prevention through digital therapy for highrisk individuals: Study protocol for the Malaysia Diabetes Prevention Programme (MyDiPP)

Novel Representation Learning Improves Personalizing Blood Test Ranges and Disease Risk Prediction

A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring

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