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Obesity01:24

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
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Multidisciplinary Approach to Obesity Management: A Case Report
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Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence.

Sarah A Graham1, Viveka Pitter1, Jonathan H Hori1

  • 1Lark Health, Mountain View, CA, USA.

Digital Health
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

An AI-powered program effectively delivers the National Diabetes Prevention Program (DPP), achieving significant weight loss comparable to traditional methods. Frequent engagement with AI coaching and weigh-ins boosts success in weight management.

Keywords:
Preventive healthcarechronic disease managementlifestyle behavior changemobile health (mHealth)obesityprediabetestype 2 diabetes

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Preventive Medicine

Background:

  • The National Diabetes Prevention Program (DPP) is effective but resource-intensive, limiting scalability.
  • An AI-powered DPP offers a potential solution for wider reach and efficiency.

Purpose of the Study:

  • To evaluate the effectiveness of an AI-powered DPP (Lark DPP) in weight loss maintenance.
  • To identify predictors of weight loss success within the AI-powered program.

Main Methods:

  • Compared 12-month weight loss maintenance between CDC-qualifying and non-qualifying participants.
  • Used logistic regression to analyze predictors of weight nadir in 3148 members.

Main Results:

  • CDC qualifiers maintained significantly greater weight loss (5.3%) than non-qualifiers (3.3%) at 12 months.
  • Weight nadir was 4.2% for all members, with 35% achieving ≥5% weight loss.
  • Male sex, frequent weigh-ins, and AI coaching exchanges predicted greater weight loss.

Conclusions:

  • AI-powered DPPs can achieve weight loss and maintenance comparable to other delivery methods.
  • Engagement with AI coaching and frequent weigh-ins are key drivers of success.
  • AI offers a scalable and resource-efficient approach to combat the prediabetes epidemic.