Hypertension Prevention and Management
Conditions
Keywords
Hypertension, Blood pressure, Prevention, Management
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALBehavioral/Digital Health Intervention
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.
Sponsors
Taipei Veterans General Hospital, Taiwan
Ministry of Health and Welfare, Taiwan
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
Participants must meet all of the following criteria: 1. Aged 18 to 75 years at the time of enrollment 2. Able and willing to provide informed consent 3. Able to operate a mobile phone and interact with the digital platform 4. Willing to participate in home blood pressure monitoring
Exclusion criteria
Participants will be excluded if they meet any of the following criteria: 1. Systolic BP consistently \<120 mmHg and diastolic BP \<70 mmHg, based on baseline home monitoring (considered low BP not targeted in this study) 2. Pregnant or planning to become pregnant during the study period 3. Unable to operate a smartphone or digital device independently 4. Cognitive impairment or language barrier that prevents understanding of study procedures or informed consent 5. Severe comorbidities that may interfere with study participation or outcomes (e.g., advanced heart failure, end-stage renal disease) 6. Current participation in another interventional clinical study that could conflict with this protocol 7. Unwillingness to follow home blood pressure monitoring protocol or adhere to follow-up schedule
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Reduction in Systolic and Diastolic Blood Pressure at 6 Months by Home Blood Pressure Monitoring | 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. \[Unit of Measure: mmHg\]; \[Method of Assessment: Home blood pressure monitoring (HBPM) using certified devices (certified by Taiwan FDA)\] |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Reduction in Systolic and Diastolic Blood Pressure at 12 Months by Home Blood Pressure Monitoring | 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. \[Unit of Measure: mmHg\]; \[Method of Assessment: Home blood pressure monitoring (HBPM) using certified devices (certified by Taiwan FDA)\] |
| Improvement in Hypertension Knowledge, Attitudes, and Behaviors (KAB) | 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. \[Unit of Measure: Score on KAB questionnaire\]; \[Method of Assessment: Structured questionnaire administered digitally before and after intervention\] |
| Adherence to 722 Blood Pressure Monitoring Protocol and Medication Compliance | 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. * BP Monitoring Adherence: At least 2 measurements per occasion, twice daily (morning and evening), for 7 consecutive days. * Medication Compliance: Taking prescribed antihypertensive medication(s) as directed (correct dosage and frequency) over the same monitoring periods. 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. \[Unit of Measure: Percentage of participants\]; \[Method of Assessment: * App-based log review of BP measurements synced from devices. * Medication adherence assessed via app-based self-reports.\] |
| Change in Time in Therapeutic Range (TTR) for Systolic Blood Pressure | 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. \[Unit of Measure: Percentage of days in target range\]; \[Method of Assessment: Home blood pressure monitor data uploaded via app and analyzed based on Taiwan Hypertension Society's TTR definition\] |
Countries
Taiwan
Contacts
CONTACTHao-Min Cheng
Outcome results
None listed