•  
  •  
 

EFFECTS OF AN EIGHT-WEEK AI-GENERATED RESISTANCE TRAINING PROGRAM ON BLOOD GLUCOSE REGULATION: A CASE STUDY

Abstract

Prediabetes, elevated blood pressure, and increased cholesterol are becoming increasingly prevalent among adults, placing individuals at risk for metabolic and cardiovascular disease over time. Resistance training has been consistently shown to improve metabolic health, enhance strength, and reduce cardiometabolic risk; however, individualized program design to address specific health concerns can be a challenge in clinical and fitness settings, particularly for new professionals with limited clinical experience. The purpose of this case study was to evaluate the outcomes of an eight-week AI-generated resistance training program in a 48-year-old female with slightly elevated glucose, blood pressure, and cholesterol. Baseline and post-training assessments included body composition, cardiovascular measures, flexibility, muscular endurance, aerobic capacity, and glucose monitoring. The exercise intervention was structured using a combination of ACSM guidelines and AI-assisted program design to individualize weekly progressions, incorporate appropriate modifications, and adjust training volume. The participant completed full-body resistance training sessions twice weekly in person, combined with at-home mobility and core sessions for a total of five training days per week. Throughout the eight weeks, the participant demonstrated an improvement in resting glucose levels, supported by a 17-point decrease in the initial 4-week blood glucose average (117 mg/dL) as compared to the last 4 weeks of the program (100 mg/dL). Post-exercise glucose levels mirrored the improvements seen in resting glucose measurements. Additionally, decreases in resting blood pressure (130/69 mmHg to 120/65 mmHg), improved aerobic capacity (Bruce Ramp duration increased from ~3 to ~7 minutes), and a reduction in body fat percentage from 41.9% to 31.9% were observed. Shoulder, hip, and knee flexibility improved approximately 15 degrees on average across the three joints, while muscular endurance increased based on increased plank hold time (+76 sec), and modified push-ups performance (+11 reps). This case study supports the idea that AI-generated exercise training programs may be considered to improve health conditions secondary to improvements in overall fitness, potentially expanding targeted programming to individuals with early-stage cardiometabolic risk.

Acknowledgements

GGC Seed Grant; Dept of EXSC

This document is currently not available here.

Share

COinS