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    This study introduces personalized, data-driven activity plans using fitness tracker data. The approach optimizes daily physical activity through tailored recommendations and dynamic plan adjustments for better user adherence.

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    Area of Science:

    • Health Informatics
    • Human-Computer Interaction
    • Behavioral Science

    Background:

    • Growing interest in fitness trackers and smartphone apps for physical activity promotion.
    • Current applications offer limited, general health advice and rely on basic step counts.
    • Need for personalized, data-driven recommendations for intraday activity planning.

    Purpose of the Study:

    • To develop and implement an approach for personalized, data-driven intraday physical activity planning.
    • To generate hour-by-hour activity plans based on individual adherence probabilities.
    • To tailor notifications and dynamically adapt plans based on real-time user activity.

    Main Methods:

    • Utilizing accelerometers to estimate user activity levels.
    • Developing personalized adherence probability models based on past activity patterns and targets.
    • Implementing dynamic plan updates based on deviations from the planned activity.

    Main Results:

    • Identification of typical activity patterns from historical user data.
    • Prediction of user achievement for activity targets.
    • Successful adaptation of activity plans based on real-time performance.

    Conclusions:

    • The developed approach enables personalized and adaptive physical activity planning.
    • Data-driven insights can enhance user engagement and adherence to activity goals.
    • This method offers a foundation for more sophisticated health behavior interventions.