Taipei Veterans General Hospital
Taipei, Taiwan
Location status: Recruiting
NCT Number: NCT07147413
This pilot study evaluated the effectiveness of an AI health education assistant compared to case manager support in hypertension management. Key outcomes included changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP), compliance with health behaviors, 722 goal achievement (monitoring, measurements, lifestyle improvements), and Technology Acceptance Model (TAM) metrics, including perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI). This study aims to examine whether the AI assistant group will achieve greater reductions in systolic and diastolic blood pressure compared to the case manager group. Additionally, it will evaluate whether the AI assistant group will demonstrate higher engagement in 722 goals and greater perceived ease of use. The results of this study are expected to provide further evidence supporting the potential of AI-driven interventions.
Interested in participating?
Request Info18 year–75 year
All sexes
Interventional
Not applicable
Taipei, Taiwan
Location status: Recruiting
Background: This pilot study aimed to evaluate the effectiveness of an AI health education assistant compared with case manager support in managing hypertension. The primary focus was on changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP), compliance with health behaviors, engagement in 722 goal achievement (monitoring, measurements, and lifestyle improvements), and Technology Acceptance Model (TAM) metrics, including perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI). This study aims to examine whether the AI assistant group will achieve greater reductions in systolic and diastolic blood pressure compared to the case manager group. Additionally, it will evaluate whether the AI assistant group will demonstrate higher engagement in 722 goals and greater perceived ease of use.
Methods: This two-arm, randomized controlled pilot study enrolled participants aged 18-75 years with hypertension. Participants were allocated to either the AI health education assistant group or the case manager group. The AI group received app-based interventions, including medication reminders, behavioral nudges, educational quizzes, and abnormal blood pressure alerts powered by AI-driven monitoring. The case manager group received personalized lifestyle advice, medication reminders, and regular follow-ups from human case managers. Blood pressure readings were collected at baseline and 6 months, and TAM metrics were assessed through questionnaires. Compliance with health behaviors and engagement in 722 goals were evaluated through app interaction logs. The sample size was determined based on a non-inferiority design comparing two independent groups. The primary outcome was the change in systolic blood pressure (SBP), which was defined as the post-intervention value minus the pre-intervention value. A one-sided significance level of 0.05 and a statistical power of 80% were assumed. The sample size calculation used a pooled standard deviation of 11.6 mmHg derived from the pilot study. A non-inferiority margin of 3 mmHg and an expected mean difference of 1.34 mmHg between the AI intervention group and the control group were also assumed. The required sample size was estimated to be 604 participants per group. After allowing for a 30% dropout rate, 863 participants will be required in each group, resulting in a total sample size of 1,726 participants for the two groups. Statistical analyses included paired t-tests for within-group changes, independent t-tests for between-group comparisons, and linear regression models to assess associations between intervention type and TAM metrics, adjusting for baseline values.
Discussion: This study will provide preliminary evidence on the feasibility and potential benefits of AI-driven interventions for hypertension management. It will further evaluate the AI assistant's ability to improve blood pressure control, increase engagement in 722 goals, and enhance technology acceptance. These findings will deliver important insights for clinicians and inform future clinical practice.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Participants must meet all of the following criteria:
Exclusion criteria
Participants will be excluded if they meet any of the following criteria:
Participants in this group will utilize an AI-powered digital health management platform designed to optimize blood pressure control. The platform provides personalized medication and blood pressure measurement reminders, tailored health education, and interactive quizzes to improve hypertension awareness and engagement. Data from blood pressure monitors will be scanned and uploaded, and the system will alert participants and case managers to abnormal readings, enabling timely interventions.
Time frame: 6 months
Change in average systolic and diastolic blood pressure (SBP and DBP) from baseline to 6 months, measured using validated automated home blood pressure monitors. Participants will follow the 722 protocol (2 readings per occasion, twice daily, over 7 consecutive days). Data are collected via the digital health platform and automatically uploaded. Outcome is calculated as the difference in mean SBP between baseline and 6-month readings. [Unit of Measure: mmHg]; [Method of Assessment: Home blood pressure monitoring (HBPM) using certified devices (certified by Taiwan FDA)]
Time frame: 12 months
Change in average systolic and diastolic blood pressure (SBP and DBP) from baseline to 12 months, measured using validated automated home blood pressure monitors. Participants will follow the 722 protocol (2 readings per occasion, twice daily, over 7 consecutive days). Data are collected via the digital health platform and automatically uploaded. Outcome is calculated as the difference in mean SBP between baseline and 6-month readings. [Unit of Measure: mmHg]; [Method of Assessment: Home blood pressure monitoring (HBPM) using certified devices (certified by Taiwan FDA)]
Time frame: 6 months
Change in participants' scores on a validated Hypertension Knowledge, Attitudes, and Behaviors (KAB) questionnaire between baseline and 6-month follow-up. The questionnaire assesses understanding of blood pressure management, behavioral intentions, and lifestyle practices. Statistical evaluation using Wilcoxon Signed-Rank Test. [Unit of Measure: Score on KAB questionnaire]; [Method of Assessment: Structured questionnaire administered digitally before and after intervention]
Time frame: 6 months
Proportion of participants who complete home blood pressure monitoring in accordance with the 722 protocol and demonstrate adherence to their prescribed antihypertensive medication regimen.
Adherence is defined as meeting both criteria at baseline and again at 6 months. This measure reflects participants' engagement with blood pressure self-monitoring behaviors and their consistency in following prescribed treatment.
[Unit of Measure: Percentage of participants]; [Method of Assessment:
Time frame: Baseline and 6 months
Change in the proportion of days during which a participant's home systolic blood pressure readings are within the target range (110-130 mmHg) over a 7-day observation period at baseline and at 6 months. TTR is calculated as the number of days with SBP within target range divided by total valid measurement days (minimum of 4 valid days required per observation period). Improvement in TTR reflects better blood pressure stability.
[Unit of Measure: Percentage of days in target range]; [Method of Assessment: Home blood pressure monitor data uploaded via app and analyzed based on Taiwan Hypertension Society's TTR definition]
Contact information is provided by the study sponsor or research team.
Taipei Veterans General Hospital, Taiwan
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