Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data (Preprint)

• DOI: 10.2196/preprints.33189
• Publication Date: 2021-09-08T21:35:13Z
• Authors: Dan Wu; Xiaoyuan Huyan; Yutong She; Junbin Hu; Huilong Duan; Ning Deng
• Keywords: mHealth; Self-Management

<sec> <title>BACKGROUND</title> Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management effective, possibly because the behavior mechanism and preferences of with various characteristics in hypertension were unclear. </sec> <title>OBJECTIVE</title> The purpose this study was (1) explore patient multibehavior engagement trails pathway-based self-management, (2) discover preference patterns, (3) identify different preferences. <title>METHODS</title> This included 863 hypertensive who generated 295,855 use records mHealth app from December 28, 2016, July 2, 2020. Markov chain used infer trails, which contained type, quantity, time spent, sequence, transition probability value (TP value) behavior. K-means algorithm group by normalized features: number behavioral states that performed each trail. pages represented states. Chi-square tests, Z-test, analyses variance, Bonferroni multiple comparisons conducted characterize patterns. <title>RESULTS</title> analysis revealed 3 types (1-way transition, cycle self-transition) 4 engagement. In perform task trail (PT-T), preferred start self-management blood pressure (BP), drug, weight 0.29, 0.18, 0.20, respectively), spent more on food state (35.87 s). Some entered BP drug 0.25) reminder item state. result-oriented (RO-T), energy ranking (19.66 s) compared report (13.25 knowledge learning (KL-T), there high 0.47, 0.31) between list content. support acquisition (SA-T), self-transition questionnaire 0.29) Cluster discovered patterns: PT-T cluster, KL-T SA-T cluster. There statistically significant associations pattern gender, education level, BP. <title>CONCLUSIONS</title> identified dynamic, longitudinal, multidimensional Patients focus BP, medications, conditions paid attention medications using reminders. diet questionnaires complicated difficult implement record. Competitive methods such as likely attract pay their own Female lower level poorly controlled be highly involved education.