Evidence map›Paper›PMID 40338170›Full record

Observational studyJMIR formative research2025

Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study.

Michelle R Jospe, Martin Kendall, Susan M Schembre, Melyssa Roy

Abstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Michelle R JospeGeorgetown Lombardi Cancer Center, Georgetown University, 3800 Reservoir Rd NW, Washington, DC, 20007, United States, 1 202-444-2223.ORCID 0000-0003-0393-5517
Martin KendallOptimising Nutrition LLC, Brisbane, Australia.ORCID 0009-0004-2546-5151
Susan M SchembreGeorgetown Lombardi Cancer Center, Georgetown University, 3800 Reservoir Rd NW, Washington, DC, 20007, United States, 1 202-444-2223.ORCID 0000-0002-9945-7136
Melyssa RoyDepartment of Medicine, University of Otago, Dunedin, New Zealand.ORCID 0000-0003-1399-3091

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Objective: This study aimed to describe user demographics, app engagement, adherence to glucose monitoring, and the resulting impact on weight and glucose levels. Methods: 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. Results: 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. Conclusions: 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.

Indexed as

Blood GlucoseFastingMobile ApplicationsAdultBlood Glucose Self-MonitoringBody WeightFemaleHumansHungerMaleMiddle AgedBlood Glucoseapp engagementbiological feedbackblood glucoseblood glucose self-monitoringdietdietary interventiondigital healthglucoseglucose monitoringmetabolic healthmonitoringpersonalized nutritionprecision healthself monitoring

Identifiers

PMID40338170
PMCPMC12080008

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.