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Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in

Dan Wu1, Xiaoyuan Huyan2, Yutong She1

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This study analyzed mobile health app usage in hypertension patients, revealing distinct engagement patterns and preferences for self-management behaviors. Understanding these preferences can optimize mHealth interventions for better hypertension control.

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data analysisengagementhypertensionmobile healthpatient behavior

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Cardiovascular Health

Background:

  • Hypertension requires continuous self-management, often supported by mobile health (mHealth) services.
  • Effectiveness of mHealth is limited by unclear patient behavior mechanisms and preferences.
  • Understanding patient characteristics is crucial for tailoring hypertension management strategies.

Purpose of the Study:

  • To explore patient engagement trails in pathway-based hypertension self-management.
  • To identify distinct patient behavior preference patterns within mHealth applications.
  • To characterize patients associated with different behavior preference patterns.

Main Methods:

  • Analysis of 295,855 use records from 863 hypertensive patients using an mHealth app.
  • Application of Markov chain to infer patient behavior engagement trails (type, quantity, time, sequence, transition probability).
  • K-means clustering for grouping patients based on normalized behavior preference features and statistical tests for characterization.

Main Results:

  • Identified 4 patient multibehavior engagement trails: Perform Task Trail (PT-T), Result-Oriented Trail (RO-T), Knowledge Learning Trail (KL-T), and Support Acquisition Trail (SA-T).
  • Patients prioritized tasks related to blood pressure (BP), medication, and weight, with high engagement in ranking and knowledge cycling.
  • Discovered 3 patient behavior preference patterns (PT-T; PT-T & KL-T; PT-T & SA-T), significantly associated with gender, education, and BP control.

Conclusions:

  • Patient behavior in mHealth hypertension management is dynamic and multidimensional, focusing on BP, medications, and weight.
  • Ranking features and health education content attract patient engagement, while diet and questionnaire features present challenges.
  • Female patients with lower education and poorer BP control show higher engagement in health education, indicating a need for tailored interventions.