Health is personal — and the tools designed to support it should reflect as much. By delivering educational content tailored to each user’s unique needs and preferences, digital health products can more effectively guide individuals on their health journeys. But personalizing educational health content isn’t just about offering general tips. It's about integrating behavioral data, preferences, and engagement patterns to ensure every recommendation is meaningful and relevant.
Holistic Support through Personalized Health Recommendations
At Significo, we focus on providing personalized recommendations that span multiple aspects of health — such as fitness, nutrition, substance use, resilience and mental well-being — tailored to each user. The term "recommendations" refers to the actionable advice or content our products provide, based on data-driven insights, to help users make more informed decisions about their health.
To create a holistic experience, our recommendations address individual health challenges by analyzing behavior data. Our recommendation engine offers specific guidance for users with significant health needs, while providing adapted advice for those with fewer challenges. This approach allows us to tailor the content to each user, making it more relevant and effective.
The personalization of these recommendations is powered by a combination of machine learning models and expert systems. These systems assess the relevance of content based on the latest health status data of the user, recorded in-app behavior, and the user’s preferences. The user’s personal interests balance medical necessity with what keeps the user engaged. For example, if a user indicates a preference like “I’m interested in learning about alcohol consumption,” our recommendation engine will tailor content to focus on that topic.
Ultimately, our goal is to provide dynamic, evolving recommendations that continuously improve based on a user’s interactions and progress — delivering a truly personalized experience that adapts to each user’s health needs over time.
How Significo’s Recommendation Engine Works
Significo’s recommendation engine is designed to enhance user engagement and health outcomes by leveraging a blend of expert models and machine learning algorithms. These models and algorithms are designed to balance recommendations that come from “health status,” “preferences,” and “in-app behavior.” They analyze vast amounts of data from user interactions within the tool, including responses to questionnaires and input from health tracking devices like Google Fit or Apple Health. By assessing these three key dimensions — health relevance, user preferences, and interaction patterns — the engine ensures that the recommendations align with both the user’s interests and engagement habits.
This approach identifies trends in user health behavior, allowing the engine to refine and prioritize recommendations based on their relevance and impact. Unlike many competitors who focus on just a few health aspects, Significo’s recommendation engine considers 15 different health factors. As a result, our approach delivers a diverse range of suggestions across various health and wellness areas, keeping the user experience dynamic and engaging. Further mechanisms such as vetoes and cooldowns ensure that users don’t receive repetitive or inappropriate advice (e.g., recommending meat to a vegan or “stop smoking” to a non-smoker), preventing disengagement caused by irrelevant or monotonous content.
Aligning Content with User Lifestyles
Users don’t just need health advice, they need advice that fits their lifestyle. Data from health trackers like Apple Health, Fitbit, or Apple Watch offer a window into users’ activity patterns, allowing recommendations to be customized to their preferences. For example, if a user tends to be more active in the evening, the engine has the ability to take that preference into account, offering suggestions that align with their habits and lifestyle. This adaptation ensures that suggestions feel relevant and actionable, helping users integrate health advice into their daily routines seamlessly.
Continuous Content Expansion to Stay Relevant
To stay ahead, content creation must be responsive and adaptable. Significo’s content creators ensure trending health topics are continuously incorporated into our system — not just for popularity, but for relevance and scientific validation. Our recommendations are built on evidence-based insights that follow health standards, like WHO guidelines, and include motivational strategies such as barrier management, self-efficacy reinforcement, and social support. Through a data-driven process, Significo also identifies areas for improvement and updates recommendations according to the latest medical guidelines within our content management system. This ensures users receive timely, evidence-based health guidance tailored to their needs.
Building on User Strengths, to Work on Weaknesses
Personalization should balance reinforcing users’ strengths with addressing areas for improvement. Significo’s recommendation engine takes this approach by tailoring content to individual user profiles. For example, a user excelling at integrating daily movement might receive increasingly advanced activity recommendations to sustain progress. Simultaneously, the engine can identify challenges, such as smoking, and provide targeted strategies to address those behaviors. By offering solution-oriented content that adapts to both strengths and areas for growth, the engine ensures health improvements feel achievable, motivating users to make meaningful progress across all aspects of their wellness journey.
Incorporating Motivational Models into Health Content
To motivate users, it’s essential to understand whether they’re likely to adopt new behaviors. Significo leverages motivational models such as the Health Action Process Approach (HAPA) to make recommendations more impactful. HAPA focuses on guiding users from intention to action by emphasizing self-efficacy, barrier management, and the importance of social support. By tailoring advice to where users are in their health actionability , we ensure they receive tailored support that’s relevant to their current needs.
While we are currently focused on HAPA,, we are also reviewing other models like the Transtheoretical Model (TTM) due to its similarities. Ultimately, HAPA allows us to meet users where they are, offering targeted guidance that encourages behavior change and helps users move forward on their health journey.
Adapting to Real-Time Changes in User Behavior
Health journeys aren’t static, and support shouldn’t be either. By continuously collecting data from health trackers and questionnaires, recommendations can evolve in real time, aligning with a user’s current health status and personal development. This adaptability ensures that delivered content remains relevant as users progress, offering timely support when they need it most. Coaching tools within the app further enable users to create personalized solutions, set goals, and track their progress, providing motivation for sustainable habit change.
Structured Coaching Frameworks for Personalized Support
To make the health journey feel even more personalized, Significo integrates recommendations within a coaching framework. This design provides actionable steps, similar to how a coach would guide a person. The coaching framework helps users take meaningful steps toward their health goals while also helping them understand their health needs and preferences. While we are still working on fully guiding users in setting realistic goals, the framework encourages them to take ownership of their health journey. By turning abstract recommendations into concrete actions, it fosters a sense of empowerment and engagement, helping users make lasting progress.
Significo’s personalized digital health products use a multi-layered approach to deliver tailored educational content that not only addresses users’ current health needs but also evolves as they progress. In our next blog, we’ll explore how we implement this approach in health application interactions.
This blog is part of a series on Personalization by Significo. Explore more in this series:
Tips for Personalization in Digital Health Product Development
Benefits of Personalization in Digital Health Products
Significo: 5 Principles for Personalization in Digital Health Products
Personalization in the digital health industry
The User Has A Name! Basic Personalization Strategies
For more on Significo’s personalization capabilities, visit Significo.com/solutions.
