Cited by 20Mobile Health and mHealth ApplicationsDiabetes Management and EducationDiet and metabolism studies

Evaluation of the Low Carb Program Digital Intervention for the Self-Management of Type 2 Diabetes and Prediabetes in an NHS England General Practice: Single-Arm Prospective Study

Open access
Summers, Charlotte, Tobin, Simon, Unwin, David·2021·JMIR Diabetes

Published version of a preprint

Abstract

BACKGROUND Type 2 diabetes mellitus has serious health consequences, including blindness, amputation, and stroke. Researchers and clinicians are increasingly in agreement that type 2 diabetes may be effectively treated with a carbohydrate-reduced diet. Digital apps are increasingly used as an adjunct to traditional health care provisions to support remote self-management of long-term health conditions. OBJECTIVE Our objective was to evaluate the real-world 12-month outcomes of patients prescribed the Low Carb Program digital health intervention at a primary care National Health Service (NHS) site. The Low Carb Program is a nutritionally focused, 12-session, digitally delivered, educational behavior change intervention for glycemic control and weight loss for adults with prediabetes and type 2 diabetes. The program educates and supports sustainable dietary changes focused on carbohydrate restriction by utilizing behavior change techniques, including goal setting, peer support, and behavioral self-monitoring, as well as personalized downloadable resources, including recipes and meal plans tailored to ethnicity, weekly shopping budget, and dietary preferences. METHODS This study evaluated the real-world outcomes of patients recruited to the Low Carb Program at an NHS general practice in Southport, United Kingdom. All of the NHS patients recruited to the program were diagnosed with type 2 diabetes or prediabetes and were given access to the program at no cost. A total of 45 participants, with a mean age of 54.8 years (SD 13.2), were included in the study. Women made up 42% (19/45) of the sample. The mean hemoglobin A1c (HbA1c) of the sample was 56.7 mmol/mol (SD 16.95) and the mean body weight was 89.4 kg (SD 13.8). RESULTS Of the 45 study participants recruited to the program, all of them (100%) activated their accounts and 37 (82%) individuals reported outcomes at 12 months. All 45 (100%) patients completed at least 40% of the lessons and 32 (71%) individuals completed more than nine out of 12 core lessons of the program. Glycemic control and weight loss improved, particularly for participants who completed more than nine core lessons in the program over 12 months. The mean HbA1c went from 58.8 mmol/mol at baseline to 54.0 mmol/mol, representing a mean reduction of 4.78 mmol/mol (SD 4.60; t31=5.87; P<.001). Results showed an average total body weight reduction of 4.17%, with an average weight reduction of 3.85 kg (SD 2.49; t31=9.27; P<.001) at the 12-month follow-up point. CONCLUSIONS A digital app prescribed to adults with type 2 diabetes and prediabetes in a primary care setting supporting a transition to a low-carbohydrate diet has been shown to be effective in improving glycemic control and enabling weight loss. Further research to understand more about factors affecting engagement with the app and further positive health implications would be valuable.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/25751

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In this review, AI used with wearables showed positive effects on glycemic status.

    “In our review, AI used with wearables showed positive effects on glycemic status [46,56].”

    From Digital Health Interventions to Prevent Type 2 Diabetes Mellitus: Systematic Review (2025) · cites this paper as [56]

    The Results > Health Outcomes section reports improved HbA1c among Low Carb Program participants. The Introduction > The Low Carb Program says users can connect wearables, and its architecture figure labels AI, but the paper does not evaluate whether AI used with wearables produced the glycemic improvement. Its single-arm design also does not support a causal conclusion. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Summers, C., Tobin, S., & Unwin, D.
Affiliations
Coventry (United Kingdom) (GB)
Corresponding author
Summers, Charlotte
Venue
JMIR Diabetes
Published
2021-09-09
Volume
6
Issue
3
Pages
e25751
ISSN
2371-4379 (ISSN-L)
OpenAlex
W3177236636
Cited by
19
References
42
Citations by year
2026: 2 · 2025: 8 · 2024: 3 · 2023: 5 · 2022: 1
FWCI
2.53
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Diabetes (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Physiology › Diet and metabolism studies
Keywords
type 2 diabetes, prediabetes, low-carbohydrate diet, carbohydrate restriction, digital health interventions, self-management, glycemic control, HbA1c, weight loss, behaviour change techniques, peer support, self-monitoring, general practice, primary care, real-world outcomes, NHS England, ethnic minority patients, meal plans, dietary behavior change

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review