Jospe Michelle R, Kendall Martin, Schembre Susan M, Roy Melyssa
Georgetown Lombardi Cancer Center, Georgetown University, 3800 Reservoir Rd NW, Washington, DC, 20007, United States, 1 202-444-2223.
Optimising Nutrition LLC, Brisbane, Australia.
JMIR Form Res. 2025 May 8;9:e65368. doi: 10.2196/65368.
The Data-Driven Fasting (DDF) app implements glucose-guided eating (GGE), an innovative dietary intervention that encourages individuals to eat when their glucose level, measured via glucometer or continuous glucose monitor, falls below a personalized threshold to improve metabolic health. Clinical trials using GGE, facilitated by paper logging of glucose and hunger symptoms, have shown promising results.
This study aimed to describe user demographics, app engagement, adherence to glucose monitoring, and the resulting impact on weight and glucose levels.
Data from 6197 users who logged at least 2 days of preprandial glucose readings were analyzed over their first 30 days of app use. App engagement and changes in body weight and fasting glucose levels by baseline weight and diabetes status were examined. Users rated their preprandial hunger on a 5-point scale.
Participants used the app for a median of 19 (IQR 9-28) days, with a median of 7 (IQR 3-13) weight entries and 52 (IQR 25-82) glucose entries. On days when the app was used, it was used a median of 1.8 (IQR 1.4-2.1) times. A significant inverse association was observed between perceived hunger and preprandial glucose concentrations, with hunger decreasing by 0.22 units for every 1 mmol/L increase in glucose (95% CI -0.23 to -0.21; P<.001). Last observation carried forward analysis resulted in weight loss of 0.7 (95% CI -0.8 to -0.6) kg in the normal weight category, 1 (95% CI -1.1 to -0.9) kg in the overweight category, and 1.2 (95% CI -1.3 to -1.1) kg in the obese category. All weight changes nearly doubled when analyzed using a per-protocol (completers) analysis. Fasting glucose levels increased by 0.11 (95% CI 0.09-0.12) mmol/L in the normal range and decreased by 0.14 (95% CI -0.16 to -0.12) mmol/L in the prediabetes range and by 0.5 (95% CI -0.58 to -0.42) mmol/L in the diabetes range. Per-protocol analysis showed fasting glucose reductions of 0.26 (SD 4.7) mg/dL in the prediabetes range and 0.94 (16.9) mg/dL in the diabetes range.
The implementation of GGE through the DDF app in a real-world setting led to consistent weight loss across all weight categories and significant improvements in fasting glucose levels for users with prediabetes and diabetes. This study underscores the potential of the GGE to facilitate improved metabolic health.
数据驱动禁食(DDF)应用程序实施葡萄糖引导饮食(GGE),这是一种创新的饮食干预措施,鼓励个体在通过血糖仪或连续血糖监测仪测得的血糖水平降至个性化阈值以下时进食,以改善代谢健康。借助纸质记录血糖和饥饿症状进行的使用GGE的临床试验已显示出有前景的结果。
本研究旨在描述用户人口统计学特征、应用程序参与度、血糖监测依从性以及对体重和血糖水平的影响。
分析了6197名至少记录了2天餐前血糖读数的用户在应用程序使用的前30天的数据。研究了应用程序参与度以及根据基线体重和糖尿病状态的体重和空腹血糖水平变化。用户以5分制对餐前饥饿程度进行评分。
参与者使用该应用程序的中位数为19(四分位间距9 - 28)天,体重记录中位数为7(四分位间距3 - 13)次,血糖记录中位数为52(四分位间距25 - 82)次。在使用该应用程序的日子里,使用次数中位数为1.8(四分位间距1.4 - 2.1)次。观察到感知饥饿与餐前血糖浓度之间存在显著的负相关,血糖每升高1 mmol/L,饥饿程度降低0.22个单位(95%置信区间 - 0.23至 - 0.21;P <.001)。末次观察结转分析结果显示,正常体重类别体重减轻0.7(95%置信区间 - 0.8至 - 0.6)kg,超重类别体重减轻1(95%置信区间 - 1.1至 - 0.9)kg,肥胖类别体重减轻1.2(95%置信区间 - 1.3至 - 1.1)kg。使用符合方案(完成者)分析时,所有体重变化几乎翻倍。正常范围内空腹血糖水平升高0.11(95%置信区间0.09 - 0.12)mmol/L,糖尿病前期范围内降低0.14(95%置信区间 - 0.16至 - 0.12)mmol/L,糖尿病范围内降低0.5(95%置信区间 - 0.58至 - 0.42)mmol/L。符合方案分析显示,糖尿病前期范围内空腹血糖降低0.26(标准差4.7)mg/dL,糖尿病范围内降低0.94(16.9)mg/dL。
在现实环境中通过DDF应用程序实施GGE可使所有体重类别持续减重,并使糖尿病前期和糖尿病用户的空腹血糖水平显著改善。本研究强调了GGE在促进改善代谢健康方面的潜力。