Result: Unlocking Potential: Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic.

Title:
Unlocking Potential: Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic.
Authors:
Kannenberg S; Diabetes Plus, Diabetology Medical Practice, Lübeck, Germany., Voggel J; Research & Development, Perfood GmbH, Lübeck, Germany., Thieme N; Research & Development, Perfood GmbH, Lübeck, Germany., Witt O; Research & Development, Perfood GmbH, Lübeck, Germany., Pethahn KL; Research & Development, Perfood GmbH, Lübeck, Germany., Schütt M; Diabetes Plus, Diabetology Medical Practice, Lübeck, Germany., Sina C; Institute of Nutritional Medicine, University Hospital Schleswig-Holstein, Lübeck Campus & University of Lübeck, Lübeck, Germany., Freckmann G; Institut für Diabetes-Technology, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany., Schröder T; Research & Development, Perfood GmbH, Lübeck, Germany.; Institute of Nutritional Medicine, University Hospital Schleswig-Holstein, Lübeck Campus & University of Lübeck, Lübeck, Germany.
Source:
Journal of diabetes science and technology [J Diabetes Sci Technol] 2026 Jan; Vol. 20 (1), pp. 113-123. Date of Electronic Publication: 2024 Jul 30.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Sage Country of Publication: United States NLM ID: 101306166 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1932-2968 (Electronic) Linking ISSN: 19322968 NLM ISO Abbreviation: J Diabetes Sci Technol Subsets: MEDLINE
Imprint Name(s):
Publication: 2014- : Thousand Oaks, CA : Sage
Original Publication: Foster City, CA : Diabetes Technology Society
MeSH Terms:
Diabetes Mellitus, Type 2*/therapy , Diabetes Mellitus, Type 2*/blood , Algorithms* , Precision Medicine*/methods , Life Style*, Humans ; Male ; Female ; Middle Aged ; Glycated Hemoglobin/analysis ; Blood Glucose Self-Monitoring/methods ; Aged ; Blood Glucose/analysis ; Artificial Intelligence ; Quality of Life ; Mobile Applications ; Prediction Algorithms
Contributed Indexing:
Keywords: continuous glucose monitoring; digital health application; digital therapeutic; glucose prediction; glucura; low-glycemic diet
Substance Nomenclature:
0 (Glycated Hemoglobin)
0 (Blood Glucose)
0 (hemoglobin A1c protein, human)
Entry Date(s):
Date Created: 20240731 Date Completed: 20260107 Latest Revision: 20260107
Update Code:
20260107
PubMed Central ID:
PMC11571624
DOI:
10.1177/19322968241266821
PMID:
39080863
Database:
MEDLINE

Further Information

Background: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D).
Method: A study of 118 participants with non-insulin-treated T2D (HbA <subscript>1c</subscript> ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA <subscript>1c</subscript> of 7.46% (0.93) used the DTx for three months to evaluate clinical endpoints, such as HbA <subscript>1c</subscript> , body weight, quality of life and app usage, for a pre-post comparison. The study also included an assessment of initial long-term data from a second use of the DTx.
Results: After three months of using the DTx, there was an improvement of 0.67% HbA <subscript>1c</subscript> in the complete cohort and -1.08% HbA <subscript>1c</subscript> in patients with poorly controlled diabetes (BL-HbA <subscript>1c</subscript> ≥ 7.0%) compared with standard of care ( P < .001). The number of patients within the therapeutic target range (< 7.0%) increased from 38% to 60%, and 33% were on the way to remission (< 6.5%). Patients who used the DTx a second time experienced a reduction of -0.76% in their HbA <subscript>1c</subscript> levels and a mean weight loss of -6.84 kg after six months ( P < .001) compared with BL.
Conclusions: These results indicate that the DTx has clinically relevant effects on glycemic control and weight reduction for patients with both well and poorly controlled diabetes, whether through single or repeated usage. It is a noteworthy improvement in T2D management, offering a non-pharmacological, fully digital solution that integrates biofeedback through CGM and an advanced AI algorithm.

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.V., N.T., K.L.P., O.W., and T.S. are employed at Perfood GmbH. T.S. and C.S. are co-founders of Perfood GmbH and minority shareholders. G.F. is general manager and medical director of the Institute for Diabetes Technology (Institut für Diabetes-Technologie (IfDT) Forschungs-und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. G.F. and IfDT have received speakers’ honoraria or consulting fees in the last three years from the following companies: Abbott, Berlin Chemie, Boydsense, Dexcom, Lilly Deutschland, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, and Ypsomed. M.S. runs an Institute for Diabetology and has received speakers’ honoraria or consulting fees in the last three years from Novo Nordisk, Lilly Deutschland, Astra Zeneca, Sanofi, Boehringer Ingelheim, Berlin Chemie, Novartis, and Perfood.