Dario Demonstrates Clinically Meaningful Blood Glucose Improvements and Personalized Glycemic Trajectories Across 22,000+ Users: Machine Learning Study Findings Published in Frontiers in Digital Health

Rhea-AI Summary

DarioHealth (NASDAQ: DRIO) published a peer-reviewed ML study in Frontiers in Digital Health on March 10, 2026, analyzing real-world data from 22,414 adults with type 2 diabetes.

Researchers used generalized linear mixed-effects tree models and found sustained blood glucose improvements linked to higher digital engagement; 12 measurements per month emerged as an actionable threshold for glycemic improvement.

Positive

• Real-world analysis of 22,414 adults with type 2 diabetes
• 12 measurements per month identified as a threshold tied to improvement
• Higher digital engagement correlated with stronger, durable glycemic gains
• Use of advanced ML models (generalized linear mixed-effects tree models) to personalize trajectories

Negative

• Observational design limits causal inference between engagement and glucose outcomes
• No randomized control or explicit effect-size percentage provided to quantify clinical magnitude

News Market Reaction – DRIO

-4.29%

3 alerts

-4.29% Session close to close

-3.3% Trough Tracked

$61.50M Market Cap

1.0x Rel. Volume

In the Mar 10 session, DRIO declined 4.29%, reflecting a moderate negative market reaction. Argus tracked a trough of -3.3% from its starting point during tracking. Our momentum scanner triggered 3 alerts that day, indicating moderate trading interest and price volatility.

Data tracked by StockTitan Argus on the day of publication.

Market Context

This announcement highlights AI-driven analysis of 22,414 type 2 diabetes users, finding distinct gl...

Analysis

This announcement highlights AI-driven analysis of 22,414 type 2 diabetes users, finding distinct glycemic trajectories and a practical engagement threshold of 12 blood glucose measurements per month. It reinforces DarioHealth’s strategy of pairing machine learning with real‑world data, consistent with prior AI-tag milestones. Investors may watch how employers and health plans respond, alongside developments in capital structure, registered resales of 2,713,180 shares, and progress toward narrowing operating losses.

Key Figures

Study population: 22,414 adults Engagement threshold: 12 measurements per month

2 metrics

Study population 22,414 adults Type 2 diabetes users with high-risk baseline blood glucose

Engagement threshold 12 measurements per month Identified as threshold for glycemic improvement

Previous AI Reports

2 past events · Latest: Dec 10 (Positive)

Same Type Pattern 2 events

Date	Event	Sentiment	24h Move	Catalyst
Dec 10	AI platform launch	Positive	+4.1%	Launch of DarioIQ conversational AI layer for hypertension members using 13B data points.
Jun 24	AI clinical data	Positive	+4.8%	AI‑powered GLP‑1 and personalization studies showing improved blood glucose and prediction accuracy.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

Prior AI-tag news for DarioHealth led to positive next-day moves of 4.11% and 4.79%, suggesting historically supportive reactions to AI-focused clinical and platform updates.

Recent Company History

Over the past year, DarioHealth has repeatedly highlighted AI-driven personalization and large real‑world datasets. On Jun 24, 2025, AI and GLP‑1 personalization findings with strong blood glucose improvements saw a 4.79% move. On Dec 10, 2025, launch of the DarioIQ AI layer, built on 13 billion data points, coincided with a 4.11% rise. Today’s AI‑tagged publication on personalized glycemic trajectories and engagement‑driven outcomes fits this evidence‑oriented trajectory.

Key Terms

machine learning, longitudinal mixed-effects analyses, generalized linear mixed-effects tree models, body mass index, +2 more

6 terms

machine learning technical

"Using advanced machine learning ("ML") models and longitudinal mixed-effects analyses"

Machine learning is a set of computer programs that learn patterns from large amounts of data and improve their predictions or decisions over time, like a recipe that gets better each time it’s adjusted based on taste tests. For investors it matters because these systems can speed up analysis, spot trends or risks humans might miss, automate routine work, and potentially create competitive advantages or cost savings that affect a company’s performance.

View in glossary

longitudinal mixed-effects analyses technical

"models and longitudinal mixed-effects analyses, researchers identified distinct glycemic trajectories"

A statistical approach that analyzes measurements taken from the same subjects over time, separating overall trends from individual variations. Think of tracking many people’s health or sales repeatedly and using a model that captures the average trajectory for the group while also allowing each person to move up or down on their own; this gives clearer, more reliable evidence about lasting effects or risks. Investors care because these analyses are commonly used in clinical trials, regulatory reviews and long-term performance studies to judge whether an outcome is consistent, durable and likely to affect future value.

generalized linear mixed-effects tree models technical

"By applying generalized linear mixed-effects tree models, researchers uncovered key moderating factors"

A statistical method that combines a decision-tree approach for splitting data into meaningful subgroups with flexible linear models that allow both overall trends and group-specific variations. Think of it as first sorting a crowd into smaller groups by shared traits, then fitting a tailored straight‑line relationship inside each group while allowing for consistent differences between groups (like regions or product lines). Investors use it to uncover hidden patterns, improve forecasts, and better measure risk across segmented portfolios or businesses.

body mass index medical

"no meaningful body mass index ("BMI") difference across ethnicities"

Body mass index (BMI) is a simple number calculated from a person’s weight and height that gives a rough measure of whether their body size is underweight, normal, overweight, or obese, similar to using a single score to gauge whether a container is underfilled or overfilled. Investors care because average BMI trends affect demand and costs in healthcare, insurance, and consumer markets, and can signal population health risks that influence long-term revenues and liabilities.

type 2 diabetes medical

"analyzed real-world data from 22,414 adults with type 2 diabetes and baseline blood glucose"

Type 2 diabetes is a chronic condition where the body struggles to control blood sugar levels because it becomes less responsive to insulin, a hormone that helps regulate sugar in the blood. It matters to investors because it can lead to increased healthcare costs, affect workforce productivity, and influence the performance of companies in the healthcare and pharmaceutical sectors. Managing or preventing the condition has significant implications for public health and economic stability.

glycemic trajectories medical

"researchers identified distinct glycemic trajectories moderated by demographic, clinical and engagement"

Glycemic trajectories are patterns of a person’s blood sugar levels over time, showing whether glucose is rising, falling, stable, or fluctuating. For investors, these patterns matter because they indicate how well a treatment, device, or lifestyle change controls blood sugar risk—similar to watching a heart rate chart to judge fitness; steady, predictable lines suggest stability and lower medical costs, while erratic or worsening trends signal higher health risks and potential market impact on related therapies or products.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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Findings reinforce that engagement data provide clinical signals directly impacting ROI

Analysis further reveals specific frequency of measurements that drive clinical outcomes and bend the cost curve

NEW YORK, March 10, 2026 /PRNewswire/ -- DarioHealth Corp. (NASDAQ: DRIO) (the "Company", "DarioHealth" or "Dario"), a leader in global digital health, today announced the publication of new peer-reviewed research in Frontiers in Digital Health demonstrating substantial and sustained blood glucose improvements among users of the Dario platform. 

The observational study, titled "Machine learning and engagement insights for personalized blood glucose management," analyzed real-world data from 22,414 adults with type 2 diabetes and baseline blood glucose levels in the high-risk range. Using advanced machine learning ("ML") models and longitudinal mixed-effects analyses, researchers identified distinct glycemic trajectories moderated by demographic, clinical and engagement factors.

By applying generalized linear mixed-effects tree models, researchers uncovered key moderating factors that influence glycemic improvement. Importantly, outcomes suggested broad across diverse user populations, as demonstrated with no meaningful body mass index ("BMI") difference across ethnicities.  Higher levels of digital engagement – specifically frequent blood glucose monitoring and lifestyle activities tags – were associated with stronger, more durable glycemic improvements.  A key, actionable insight identified 12 measurements per month as a threshold of glycemic improvement. 

"Our findings reinforce that engagement is not just a usage metric – it is a clinical signal," said Yifat Hershcovitz, PhD, VP Clinical & Scientific Affairs at Dario and senior author of the study. "We observed a substantial early reduction in blood glucose followed by sustained stabilization, particularly among users who monitored consistently and engaged with lifestyle tools. Machine learning enables us to translate these patterns into adaptive, data-driven strategies that optimize long-term diabetes management."

"This study demonstrates that digital health platforms can move beyond one-size-fits-all approaches," said Omar Manejwala, MD, Chief Medical Officer of Dario. "By applying machine learning to real-world data at scale, we can identify which users respond best, when intervention is most impactful and how engagement behaviors influence outcomes. These insights allow us to personalize support dynamically and improve blood glucose management in a clinically meaningful way." 

For employers, health plans and risk-bearing provider organizations seeking scalable, evidence-based solutions, the published findings underscore Dario's ability to translate real-world data into measurable clinical impact for clients. 

About DarioHealth Corp. (NASDAQ: DRIO)

DarioHealth Corp. (NASDAQ: DRIO) is a leading digital health company revolutionizing how people with chronic conditions manage their health through a user-centric, multi-chronic condition digital therapeutics platform. Dario's platform and suite of solutions deliver personalized and dynamic interventions driven by data analytics and one-on-one coaching for diabetes, hypertension, weight management, musculoskeletal pain and behavioral health.

Dario's user-centric platform offers people continuous and customized care for their health, disrupting the traditional episodic approach to healthcare. This approach empowers people to holistically adapt their lifestyles for sustainable behavior change, driving exceptional user satisfaction, retention and results and making the right thing to do the easy thing to do.

Dario provides its highly user-rated solutions globally to health plans and other payers, self-insured employers, providers of care and consumers. To learn more about Dario and its digital health solutions, or for more information, visit http://dariohealth.com. 

Cautionary Note Regarding Forward-Looking Statements

This news release and the statements of representatives and partners of DarioHealth Corp. related thereto contain or may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Statements that are not statements of historical fact may be deemed to be forward-looking statements. For example, the Company is using forward-looking statements in this press release when it discusses the potential benefits of its platforms, that the engagement data provides clinical signals that could impact ROI and may improve long-term glycemic outcomes, and that machine learning analytics may enable more adaptive and personalized care strategies.. Without limiting the generality of the foregoing, words such as "plan," "project," "potential," "seek," "may," "will," "expect," "believe," "anticipate," "intend," "could," "estimate" or "continue" are intended to identify forward-looking statements. Readers are cautioned that certain important factors may affect the Company's actual results and could cause such results to differ materially from any forward-looking statements that may be made in this news release. Factors that may affect the Company's results include, but are not limited to, regulatory approvals, product demand, market acceptance, impact of competitive products and prices, product development, commercialization or technological difficulties, the success or failure of negotiations and trade, legal, social and economic risks, and the risks associated with the adequacy of existing cash resources. Additional factors that could cause or contribute to differences between the Company's actual results and forward-looking statements include, but are not limited to, those risks discussed in the Company's filings with the U.S. Securities and Exchange Commission. Readers are cautioned that actual results (including, without limitation, the timing for and results of the Company's commercial and regulatory plans for Dario™ as described herein) may differ significantly from those set forth in the forward-looking statements. The Company undertakes no obligation to publicly update any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by applicable law.

DarioHealth Corporate Contacts

Michael Lipari
SVP Corporate Development
irteam@dariohealth.com
+1-201-785-6310

Rob Halpern
SVP Marketing
irteam@dariohealth.com

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SOURCE DarioHealth Corp.

FAQ

What did Dario (DRIO) publish on March 10, 2026 about blood glucose improvements?

The company reported a peer-reviewed ML study showing sustained blood glucose improvements in 22,414 adults with type 2 diabetes. According to Dario, higher digital engagement and a threshold of 12 measurements per month were linked to stronger glycemic gains.

How does the Dario study define the engagement threshold tied to better outcomes for DRIO users?

The study identifies 12 blood glucose measurements per month as a practical engagement threshold linked to improvement. According to Dario, meeting this frequency plus lifestyle activity tags correlated with more durable glycemic stabilization in real-world users.

What machine learning methods did Dario use in the Frontiers in Digital Health study (DRIO)?

Researchers applied generalized linear mixed-effects tree models and longitudinal mixed-effects analyses to real-world data. According to Dario, these ML approaches uncovered distinct glycemic trajectories moderated by demographic, clinical, and engagement factors.

Which user groups showed glycemic improvement in Dario's 22,414-person study (DRIO)?

Improvements appeared broadly across diverse user populations, with no meaningful BMI differences across ethnicities observed. According to Dario, demographic and clinical moderators influenced trajectories but benefits were not limited to a single subgroup.

What are the implications of Dario's study for employers and health plans considering DRIO?

The findings suggest scalable, evidence-based digital diabetes support can produce measurable clinical impact for clients. According to Dario, translating real-world engagement signals into personalized strategies may help improve blood glucose management at scale.