# Machine learning and engagement insights for personalized blood glucose management

## TL;DR

This study uses machine learning and digital health data to identify personalized strategies for managing blood glucose in diabetes patients.

## Contribution

The novel contribution is applying ML to real-world digital health data to uncover personalized BG trajectories and moderating factors.

## Key findings

- Users over 60 showed the largest sustained blood glucose improvements.
- Insulin users diagnosed within five years had the greatest BG reduction in the first four months.
- Frequent blood glucose monitoring was linked to greater and sustained improvements.

## Abstract

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose (BG) levels, with poor control linked to serious long-term complications. Managing BG effectively requires personalized strategies, given the influence of demographic, lifestyle, and clinical factors. Machine learning (ML) offers a powerful framework for analyzing complex, real-world data to uncover individual patterns of glycemic control. Coupled with digital health platforms that enable real-time monitoring and behavioral engagement, these tools can transform diabetes care. This study leverages data from the Dario digital health platform to examine BG trends moderated by clinical and engagement variables, aiming to inform personalized digital interventions.

To apply ML techniques to digital health data to identify moderating factors that influence individual BG trajectories, supporting data-driven and personalized diabetes management.

A retrospective cohort study was conducted using real-world data from users with type 2 diabetes and baseline BG ≥180 mg/dL who measured BG over at least two separate months between 2020 and 2024. A piecewise linear mixed-effects model characterized BG changes over time. Generalized Linear Mixed Effects Tree models identified subgroups with distinct BG trajectories based on demographic (age, gender, BMI, ethnicity), clinical (insulin use, comorbidities, diagnosis year), and monitoring factors. An additional model tested whether lifestyle engagement (e.g., meal and activity logging) moderated BG improvement across age groups.

Data from 22,414 users (49.9% male; mean age 57.5; BMI 34.5) showed significant reductions in monthly average BG over 12 months (B = –6.8 in months 1–4; B = –0.3 in months 4–12; both p < .001). Age strongly moderated outcomes; users >60 showed the largest sustained improvements. Clinical factors such as insulin use and diagnosis duration further stratified responses, with insulin users diagnosed within five years showing the greatest reduction within the first 4 months (B = –14.3, p < .001). Higher frequency of BG monitoring (>12/month) was associated with greater and sustained improvements.

Machine learning can reveal distinct glycemic trajectories moderated by demographic, clinical, and engagement factors. These findings underscore the potential of digital health platforms to personalize diabetes care and improve blood glucose management through adaptive, data-driven strategies.