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Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study

Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study

Patients were eligible if they were aged >18 years and had one or more of the following conditions: hypertension (HT; ICD-10 code I10-I16), diabetes mellitus (DM; ICD-10 code E08-E13), dyslipidemia (DLP; ICD-10 code E78), and atrial fibrillation (AF; ICD-10 code I48). Patients were excluded if they had a stroke on their first visit or only had one visit during the study period.

Sermkiat Lolak, John Attia, Gareth J McKay, Ammarin Thakkinstian

JMIR Cardio 2025;9:e50627

The Influence of Physical Activity and Diet Mobile Apps on Cardiovascular Disease Risk Factors: Meta-Review

The Influence of Physical Activity and Diet Mobile Apps on Cardiovascular Disease Risk Factors: Meta-Review

The major CVD risk factors are physical inactivity, obesity, diabetes mellitus (DM), dyslipidemia, and hypertension, with a prevalence ranging from 11% for DM to 75% for physical inactivity among US adults [1,2,4-8]. Half of US adults have 1 or more CVD risk factors [4-8]. Professional guidelines recommend lifestyle modifications, notably physical activity (PA) and diet, as critical first steps to prevent and treat CVD and its risk factors [2,9-11].

Erica Bushey, Yin Wu, Alexander Wright, Linda Pescatello

J Med Internet Res 2024;26:e51321

Objective Analysis of Traditional Chinese Medicine Syndrome Differentiation of Patients With Diabetes and Prediabetes: Protocol for a Nonrandomized, Exploratory, Observational Case-Control Study Using Digitalized Traditional Chinese Medicine Diagnostic Tools

Objective Analysis of Traditional Chinese Medicine Syndrome Differentiation of Patients With Diabetes and Prediabetes: Protocol for a Nonrandomized, Exploratory, Observational Case-Control Study Using Digitalized Traditional Chinese Medicine Diagnostic Tools

DIA: diabetes without hypertension or dyslipidemia; DIAHD: diabetes with hypertension and dyslipidemia; HEA: healthy or subhealthy; PRE: prediabetes without hypertension or dyslipidemia; PREHD: prediabetes with hypertension and dyslipidemia; TCM: traditional Chinese medicine. To ensure a sufficient sample size for statistical analysis of the diabetic and prediabetic syndrome differentiation, we intend to recruit 250 participants who will be equally assigned to one of 5 arms.

Hui Ping Ng, Shu Yun Chong, Yi Huan Li, Tong Hwee Goh, Ka Yii Pang, Michelle Jessica Pereira, Chin-Ming Huang

JMIR Res Protoc 2024;13:e56024

The Effect of Vitamin D on Inflammation and Dyslipidemia in Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-analysis of Randomized Controlled Trials

The Effect of Vitamin D on Inflammation and Dyslipidemia in Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-analysis of Randomized Controlled Trials

The research question is “Can supplementation with vitamin D improve inflammation and ameliorate dyslipidemia in T2 D?” This protocol will subscribe to PICO guidelines [26]. Participants are adult patients with T2 D. The intervention will be vitamin D supplementations of any dose, and the comparators are placebo or healthy patients without treatment. The outcomes are inflammation and dyslipidemia, and the study design includes RCTs.

Rizqah MacGirlley, Kabelo Mokgalaboni

JMIR Res Protoc 2023;12:e42193

Evaluating the Impact of a Digital Nutrition Platform on Cholesterol Levels in Users With Dyslipidemia: Longitudinal Study

Evaluating the Impact of a Digital Nutrition Platform on Cholesterol Levels in Users With Dyslipidemia: Longitudinal Study

In order to better understand how the dyslipidemia status changed over time, we calculated the percent of participants by category of change in the dyslipidemia status from the beginning to the end of the program as follows: dyslipidemia to normal, normal to dyslipidemia, dyslipidemia to dyslipidemia, and normal to normal.

Emily A A Hu, Jared Scharen, Viet Nguyen, Jason Langheier

JMIR Cardio 2021;5(1):e28392

Mapping the Evidence on the Effectiveness of Telemedicine Interventions in Diabetes, Dyslipidemia, and Hypertension: An Umbrella Review of Systematic Reviews and Meta-Analyses

Mapping the Evidence on the Effectiveness of Telemedicine Interventions in Diabetes, Dyslipidemia, and Hypertension: An Umbrella Review of Systematic Reviews and Meta-Analyses

The research question is based on the Population, Intervention, Control, Outcome, and Time (PICOT) criteria: In patients with diabetes, hypertension or dyslipidemia, what is the evidence for the effectiveness of telemedicine-supported chronic care on disease-specific clinical outcomes?

Patrick Timpel, Sarah Oswald, Peter E H Schwarz, Lorenz Harst

J Med Internet Res 2020;22(3):e16791

Therapeutic Management of Dyslipidemia Patients at Very High Cardiovascular Risk (CARDIO TRACK): Protocol for the Observational Registry Study

Therapeutic Management of Dyslipidemia Patients at Very High Cardiovascular Risk (CARDIO TRACK): Protocol for the Observational Registry Study

Dyslipidemia is an important modifiable risk factor for atherosclerotic cardiovascular disease and was the risk factor with the highest population attributable risk in the INTERHEART (Effect of Potentially Modifiable Risk Factors Associated with Myocardial Infarction) study [4,5]. The prevalence of dyslipidemia in Africa in general and South Africa specifically is increasing and is probably related to lifestyle changes secondary to rapid urbanization [4,6,7].

Poobalan Naidoo, Rashem Mothilal, Dirk Jacobus Blom

JMIR Res Protoc 2018;7(6):e163