---
title: "Multi-level Personalization Approach in Health App Interactions"
description: "Read about AnNam Labs’ two-level approach to creating a fully personalized digital health tool with a wide variety of personalization features."
canonical: https://annamlabs.com/blog/multi-level-personalization-approach-in-health-app-interactions
---


# Multi-level Personalization Approach in Health App Interactions

Read about AnNam Labs’ two-level approach to creating a fully personalized digital health tool with a wide variety of personalization features.

*AnNam Labs Editorial · November 21, 2024 · 3 min read*





#### Three levels of adaptation



| Preferences | A person chooses a language or reminder time. | Edit or reset the preference. | Is the choice easy to find later? |
| --- | --- | --- | --- |
| Context | A task resumes from its last saved step. | Review the saved information. | Does the product explain what it remembered? |
| Recommendations | The product suggests a relevant next action. | Dismiss, correct, or request another option. | Can a person understand why it was suggested? |



#### Make the boundaries visible





| First visit | Offer a clear default without unnecessary questions. | Can people begin the intended task? |
| --- | --- | --- |
| Changed preference | Apply the change consistently and explain any delay. | Do people recognise that the setting has changed? |
| Unhelpful suggestion | Allow dismissal or correction without blocking progress. | Can people continue without the suggestion? |
| Missing information | Explain the limitation in plain language. | Do people know what they can do next? |

#### Review the experience over time



- Name the difficulty that adaptation is meant to address.
- Use language that describes what changes and what remains under the person’s control.
- Keep the core task available when an adaptive feature cannot help.
- Review feedback, unintended effects, and the maintenance needed to keep the feature understandable.




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