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A Pharmacist Intervention for Monitoring and Treating Hypertension Using Bidirectional Texting

A Pharmacist Intervention for Monitoring and Treating Hypertension Using Bidirectional Texting

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03986931
Enrollment
535
Registered
2019-06-14
Start date
2020-02-25
Completion date
2023-06-22
Last updated
2025-01-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hypertension

Brief summary

Bidirectional texting is an effective way to collect home blood pressure (BP) measurements from subjects, but collecting BP measurements and sending them to physicians does not necessarily lead to decreased BP. Pharmacist interventions have been successful in decreasing subject BP. However, pharmacists are expensive, and in successful interventions, spent a substantial amount of time collecting home BP measurements. In this study, a proven pharmacist intervention will be added to a bidirectional texting program to determine if a combined pharmacist-bidirectional texting intervention is successful at decreasing subject BP and increasing subject BP treatment intensification in a cost-effective manner. This study will be a cluster-randomized, controlled trial of a new intervention.

Detailed description

HTN is associated with the greatest attributable risk for mortality among all modifiable risk factors for cardiovascular disease.\[1\] In 2014, there were approximately 72 million adults (29%) with HTN in the US.\[2\] Several clinical trials have demonstrated that antihypertensive medications reduce cardiovascular events.\[3\] For example, even a 5 mm Hg difference in systolic BP (SBP) over 3-5 years can reduce the risk of cardiovascular complications and strokes by 25-30%.\[4\] Yet, approximately 20% of U.S. adults are unaware of their HTN,\[5\] and among patients diagnosed with HTN, 47% are uncontrolled.\[2\] Thus, there is a critical need to effectively treat patients with HTN. Clinical inertia has been identified as the primary cause for delays in achieving BP control for over 20 years.\[6-8\] Providers often discount office BP readings that may be falsely elevated due to observer or measurement error and/or the clinical surroundings (e.g., white coat HTN).\[9-11\] Patients are often seen only once or twice a year which further delays BP control. Furthermore,\[8, 12\] BP goals are achieved in only 49% of the patients who take anti-hypertensive medications.\[5\] New approaches for acquiring more BP readings are also needed to better monitor and titrate treatment because a significant proportion of patients on therapy are not adequately controlled despite frequent physician visits. Home BP measurements, (i.e., having patients take their BP at home), can facilitate the more timely diagnosis of HTN by reducing diagnostic uncertainty. In fact, home measurements are better prognostic indicators of stroke and cardiovascular mortality than clinic measurements,\[13-15\] are more closely correlated with end-organ damage from HTN than clinic measurements,\[16, 17\] are cost effective and well-tolerated by patients,\[18\] and generate BP readings that are at least as reproducible as clinic readings.\[19\] Home BP measurements, if available, may help physicians overcome barriers related to clinical inertia.\[20\] However, the data must be followed by action. The researchers have pioneered physician-pharmacist collaborative management (PPCM) that has been shown to decrease clinical inertia and improve BP control.\[21, 22\] Pharmacists have been embedded within the medical office to perform BP management. The pharmacists are able to assess patients' needs and provide recommendations to physicians regarding treatment changes, providing patients with timely therapy adjustments.\[23\] However, many medical office leaders are unable to hire clinical pharmacists due to limited resources. Therefore, a virtual, remote clinical pharmacy service has been developed.\[24\] Pharmacists were able to obtain electronic medical record (EMR) access at all intervention offices for private physician offices throughout Iowa. While the physicians accepted 95% of the pharmacists' recommendations, the effect on improving BP was modest (manuscript under review). Adding the proposed texting platform with home BP monitoring should markedly improve the potency of our remote, telepharmacy intervention. This trial exhibits clinical equipoise because, although it is known that texting is an efficient method for obtaining home BP measurements, and that pharmacist interventions to improve BP are cost-effective, it is not known if combining these two interventions will also be cost effective. There are four reasons why this study might not be successful. First, while meta-analyses have found significantly improved BP with pharmacist interventions some studies were not successful.\[25\] Second, more data does not necessarily mean better data: patients could report false values, BP could be abnormally low or high during the measurement period, or the data could be ignored. Third, even better data might not lead to better outcomes: data could be ignored by pharmacists, physicians or patients; pharmacists' recommendations could be ignored by physicians or patients. Fourth, even if the study is effective at improving subject outcomes, it might not be cost effective. Texting might not save as much time as hypothesized. Thus, further research is needed to address these gaps in knowledge. There is a critical need for an easy-to-use, cost-effective, mobile health (m-health) approach to assist patients and healthcare providers with screening, diagnosis, and monitoring of HTN. Small medical offices and those located in poor or rural areas are unable to operationalize team-based care with pharmacists. The researchers have overcome this barrier with the use of a remote clinical pharmacy services. Coronary heart disease deaths could be reduced by 15-20% and stroke deaths by 20-30% if this intervention effectively improves BP and is implemented more widely in primary care offices. The goal of this proposal is to evaluate whether a scalable, short messaging service (SMS) approach combined with a pharmacist-based intervention improves BP management cost effectively. To achieve this objective, the following specific aims are proposed: 1. Determine if mean BP 12 months after the intervention decreases more for the intervention group than the control group. The working hypothesis is that those in the pharmacist-intervention group will achieve larger BP decreases than those in the control group. 2. Determine if the intervention leads to more intensification of therapy than in the control group. The working hypothesis is that subjects in the pharmacist-intervention group will have more treatment changes than those in the control group. 3. Determine the cost effectiveness of the intervention. The working hypothesis is that the intervention will be cost effective when compared to the control group.

Interventions

OTHERExperimental: Pharmacist-Bidirectional Texting Group

The goal of this intervention is to determine if bidirectional texting and pharmacist monitoring will improve blood pressure control.

This group will receive bidirectional texting, but no pharmacist monitoring.

Sponsors

National Heart, Lung, and Blood Institute (NHLBI)
CollaboratorNIH
Linnea Polgreen
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Investigator)

Masking description

Research interns conducting baseline and exit data collection will be blinded so that they do not influence responses.

Eligibility

Sex/Gender
ALL
Age
21 Years to 100 Years
Healthy volunteers
No

Inclusion criteria

* Fluent in English or Spanish * Have a clinic measured blood pressure of \> or = 145 mmHg and/or \> or = 95 mmHg at two previous clinic visits or one previous clinic visit and on the day of enrollment * Must be a patient at Family Medicine, River Crossings, Scott Blvd, or Muscatine University of Iowa Clinics * Live in a zip code that is scored as a 4-10 on the Rural-Urban Commuting Area codes

Exclusion criteria

* Currently pregnant or planning to become pregnant in the next year * Upper arm circumference greater than 50 cm (20 in) * Prisoner status * Unable to provide own informed consent

Design outcomes

Primary

MeasureTime frameDescription
Difference in Change in Systolic Blood Pressure- 12 Months12 monthsThe difference between the groups in the change from baseline to 12 months in systolic blood pressure in mm Hg.

Secondary

MeasureTime frameDescription
Difference in Change in Diastolic Blood Pressure- 12 Months12 monthsThe difference between the groups in the change from baseline to 12 months in diastolic blood pressure in mmHg.
Number of Medication Changes From 6 to 12 Months12 monthsTotal number of medication changes (i.e., dose, discontinuation, initiation, etc.) from 6 months to 12 months as documented in the medical record.
Dollars Spent Per Patient for 12 Month Bidirectional Texting/Pharmacist Intervention12 monthsTotal cost of medications, time spent by research staff, and clinics visits per patient from baseline.

Countries

United States

Participant flow

Pre-assignment details

The participant must have an average research blood pressure \> 145 mmHg systolic or \> 95 mmHg diastolic measured by a research team member on the day of enrollment to be eligible to continue participation. 535 participants signed a consent form. 114 participants did not pass the additional blood pressure screening and were not randomized. 1 participant died prior to randomization. 420 participants were randomized.

Participants by arm

ArmCount
Pharmacist-Bidirectional Texting Group
Patients enrolled in the Pharmacist-Bidirectional Texting Group will return 7 morning and 7 evening blood pressure measurements via text message. The report will be shared with a pharmacist who will monitor them for 12 months. The pharmacist will have access to their entire medical record and will provide support and education via text messaging, email, or phone calls, whichever is preferred by the patient. The pharmacist will develop a care plan and make recommendations to the physician through the electronic medical record to quickly adjust therapy to improve control. They will also recommend laboratory testing if indicated. They will have contact with the patient every 2-3 weeks while blood pressure is uncontrolled, and at least every 2 months when it is controlled. The pharmacist will track all recommendations made to physicians and whether or not they were implemented, modified, or rejected. Experimental: Pharmacist-Bidirectional Texting Group: The goal of this intervention is to determine if bidirectional texting and pharmacist monitoring will improve blood pressure control.
209
Control Group
Patients randomized to the control group will also return 7 morning and 7 evening blood pressure measurements. The report will be shared with a pharmacist who will call the patient to discuss the measurements and possibly recommend follow up with a physician, but no other pharmacist intervention or monitoring will occur during the 12 months. Active Comparator: Control Group: This group will receive bidirectional texting, but no pharmacist monitoring.
211
Total420

Baseline characteristics

CharacteristicTotalPharmacist-Bidirectional Texting GroupControl Group
10-Year Atherosclerotic Cardiovascular Disease (ASCVD) Risk16.0 Percentage
STANDARD_DEVIATION 14.5
17.3 Percentage
STANDARD_DEVIATION 14.2
15.0 Percentage
STANDARD_DEVIATION 14.7
Age, Continuous58.6 Years
STANDARD_DEVIATION 13.6
58.7 Years
STANDARD_DEVIATION 13.7
58.4 Years
STANDARD_DEVIATION 13.6
Baseline Diastolic Blood Pressure89.5 mmHg
STANDARD_DEVIATION 11.9
88.4 mmHg
STANDARD_DEVIATION 12.3
90.1 mmHg
STANDARD_DEVIATION 12.5
Baseline Systolic Blood Pressure154.3 mmHg
STANDARD_DEVIATION 14
156.0 mmHg
STANDARD_DEVIATION 13.7
153.4 mmHg
STANDARD_DEVIATION 14
Body Mass Index33.1 kg/m^2
STANDARD_DEVIATION 7.7
32.6 kg/m^2
STANDARD_DEVIATION 7.4
33.7 kg/m^2
STANDARD_DEVIATION 7.8
Ethnicity (NIH/OMB)
Hispanic or Latino
97 Participants49 Participants48 Participants
Ethnicity (NIH/OMB)
Not Hispanic or Latino
322 Participants160 Participants162 Participants
Ethnicity (NIH/OMB)
Unknown or Not Reported
1 Participants0 Participants1 Participants
Hypertension Medications1.6 medications
STANDARD_DEVIATION 1.2
1.7 medications
STANDARD_DEVIATION 1.2
1.5 medications
STANDARD_DEVIATION 1.3
Level of Education
Associate's Degree
47 Participants27 Participants20 Participants
Level of Education
Bachelor's Degree
54 Participants30 Participants24 Participants
Level of Education
Doctoral Degree
11 Participants5 Participants6 Participants
Level of Education
< High School
78 Participants32 Participants46 Participants
Level of Education
High School
122 Participants66 Participants56 Participants
Level of Education
Master's Degree
20 Participants10 Participants10 Participants
Level of Education
Some College
83 Participants37 Participants46 Participants
Level of Education
Trade School
5 Participants2 Participants3 Participants
Medical History
Angina
30 participants22 participants8 participants
Medical History
Chronic Kidney Disease
24 participants13 participants11 participants
Medical History
Coronary Artery Disease
30 participants18 participants12 participants
Medical History
Diabetes Mellitus
122 participants57 participants65 participants
Medical History
Heart Attack
14 participants10 participants4 participants
Medical History
Heart Failure
16 participants10 participants6 participants
Medical History
Hyperlipidemia
113 participants60 participants53 participants
Medical History
Stroke
16 participants11 participants5 participants
Medical History
Transient Ischemic Attack (TIA)
3 participants1 participants2 participants
Race (NIH/OMB)
American Indian or Alaska Native
2 Participants1 Participants1 Participants
Race (NIH/OMB)
Asian
3 Participants3 Participants0 Participants
Race (NIH/OMB)
Black or African American
16 Participants11 Participants5 Participants
Race (NIH/OMB)
More than one race
3 Participants3 Participants0 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants0 Participants0 Participants
Race (NIH/OMB)
Unknown or Not Reported
16 Participants7 Participants9 Participants
Race (NIH/OMB)
White
380 Participants184 Participants196 Participants
Rural-Urban Commuting Area Codes
RUCA 10
136 Participants64 Participants72 Participants
Rural-Urban Commuting Area Codes
RUCA 4
79 Participants35 Participants44 Participants
Rural-Urban Commuting Area Codes
RUCA 5
36 Participants21 Participants15 Participants
Rural-Urban Commuting Area Codes
RUCA 6
1 Participants1 Participants0 Participants
Rural-Urban Commuting Area Codes
RUCA 7
153 Participants80 Participants73 Participants
Rural-Urban Commuting Area Codes
RUCA 8
7 Participants5 Participants2 Participants
Rural-Urban Commuting Area Codes
RUCA 9
8 Participants3 Participants5 Participants
Sex: Female, Male
Female
209 Participants96 Participants113 Participants
Sex: Female, Male
Male
211 Participants113 Participants98 Participants
Smoking History
Current Smoker
71 Participants37 Participants34 Participants
Smoking History
Former Smoker
112 Participants55 Participants57 Participants
Smoking History
Never Smoked
237 Participants117 Participants120 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
deaths
Total, all-cause mortality
7 / 2094 / 211
other
Total, other adverse events
0 / 2090 / 211
serious
Total, serious adverse events
0 / 2090 / 211

Outcome results

Primary

Difference in Change in Systolic Blood Pressure- 12 Months

The difference between the groups in the change from baseline to 12 months in systolic blood pressure in mm Hg.

Time frame: 12 months

Population: Blood pressure was measured by the research team at baseline, 6 months, and 12 months. All participants had a baseline measurement. Some were missing 6- or 12-month measurements. All collected measurements were included in the model estimation.

ArmMeasureValue (MEAN)Dispersion
Pharmacist-Bidirectional Texting GroupDifference in Change in Systolic Blood Pressure- 12 Months-23.8 mmHgStandard Deviation 19.4
Control GroupDifference in Change in Systolic Blood Pressure- 12 Months-19.6 mmHgStandard Deviation 19.4
Secondary

Difference in Change in Diastolic Blood Pressure- 12 Months

The difference between the groups in the change from baseline to 12 months in diastolic blood pressure in mmHg.

Time frame: 12 months

Population: Blood pressure was measured by the research team at baseline, 6 months, and 12 months. All participants had a baseline measurement. Some were missing 6- or 12-month measurements. All collected measurements were included in the model estimation.

ArmMeasureValue (MEAN)Dispersion
Pharmacist-Bidirectional Texting GroupDifference in Change in Diastolic Blood Pressure- 12 Months-9.1 mmHgStandard Deviation 13.4
Control GroupDifference in Change in Diastolic Blood Pressure- 12 Months-9.6 mmHgStandard Deviation 11.4
Secondary

Dollars Spent Per Patient for 12 Month Bidirectional Texting/Pharmacist Intervention

Total cost of medications, time spent by research staff, and clinics visits per patient from baseline.

Time frame: 12 months

ArmMeasureValue (MEAN)Dispersion
Pharmacist-Bidirectional Texting GroupDollars Spent Per Patient for 12 Month Bidirectional Texting/Pharmacist Intervention980.17 DollarsStandard Deviation 1127.91
Control GroupDollars Spent Per Patient for 12 Month Bidirectional Texting/Pharmacist Intervention838.95 DollarsStandard Deviation 713.45
Secondary

Number of Medication Changes From 6 to 12 Months

Total number of medication changes (i.e., dose, discontinuation, initiation, etc.) from 6 months to 12 months as documented in the medical record.

Time frame: 12 months

Population: All participants had a BP medication chart review at baseline, 6 months, and 12 months unless they withdrew from the study prior to a chart review period.

ArmMeasureValue (MEAN)Dispersion
Pharmacist-Bidirectional Texting GroupNumber of Medication Changes From 6 to 12 Months0.59 Medication ChangesStandard Deviation 0.98
Control GroupNumber of Medication Changes From 6 to 12 Months0.44 Medication ChangesStandard Deviation 0.84

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026