Hypertension Clinical Trial
Official title:
Reducing Adverse Self-Medication Behaviors in Older Adults With Hypertension
The purpose of this study is to reduce adverse self-medication practices in older adults with hypertension.
BACKGROUND:
Failure of older adults to take medications properly is estimated to be a factor in more
than a quarter of emergency room visits and 10 percent of nursing home admissions, with a
total cost of over $25 billion annually. It has been estimated that 10 percent of adverse
drug events may be attributed to communication failure between the provider and patient.
Lack of adherence to prescription medication protocols and adverse self-medication practices
can result in drug interactions that can be fatal. In a health environment that promotes
polypharmacy for older adults, a group that is less well able to see, hear, and understand
medical information than the general population, a need exists to educate both older adults
and their providers about the dangers of adverse drug interactions arising from
self-medication.
DESIGN NARRATIVE:
The goal is to reduce adverse self-medication practices in older adults with hypertension. A
computer program developed for older adults (Personal Education Program or PEP) will be
modified as the next generation PEP, (PEP-NG), to include an interface to allow clients to
enter self-medication behavior data independently on a wireless, touch screen tablet
computer. Data entered on the PEP-NG will be analyzed via a secure server (ProHealth) and
program content tailored to the specific adverse self-medication behaviors will be
delivered. Summaries of the reported behaviors with corrective strategies will be printed
for both patient and provider so the advanced practice registered nurse (APRN) has a good
idea of the client's self-medication practices before beginning the face-to-face interview.
Thus, the PEP-NG will make optimal use of the client's waiting time and the APRN's clinical
time.
Specific aims are at three levels. 1) To design an interface for the PEP-NG with: a)
desirable characteristics for both older adults and primary care providers; and b) minimal
user burden. 2) To show that APRNs will increase: a) knowledge concerning potential drug
interactions arising from older adults' self-medication practices; b) self-efficacy for
teaching older adults about potential drug interactions; c) self-efficacy for communicating
with older adults about self-medication; and to d) demonstrate satisfaction using the PEP-NG
with clients. 3) To show that older adults using the PEP-NG will: a) increase knowledge
concerning potential drug interactions arising from self-medication practices; b) increase
self-efficacy as to how to avoid potential drug interactions arising from self-medication
practice; c) reduce self-reported adverse behaviors associated with potential drug
interactions; 4) improve prescription medication adherence; d) achieve target blood pressure
readings; and demonstrate e) satisfaction with the PEP-NG and f) the APRN provider
relationship. User-sensitive inclusive design methods will be used to develop and test the
patient data interface. After beta-testing the PEP-NG in the primary care laboratory, the
PEP-NG will be piloted by 10 APRNs affiliated with ProHealth primary care practices.
Repeated Measures Analysis of Variance (RM ANOVA) with one within subjects factor (TIME)
will be performed on APRNs' outcome measures at time 0, immediate post-instruction, and 3
and 6 months later. APRNs will each recruit 24 clients (240 total) meeting study criteria.
The PEP-NG software will randomly assign each APRN's clients to either control (data
collection only) and experimental (data collection plus educational intervention with
targeted messages). Client outcome will be assessed at 0, 4, 8, 12, and 52 weeks and
analyzed as a mixed ANOVA with NURSE and GROUP as between-subjects factors and TIME as a
repeated-measures factor. Healthcare utilization will be compared between the 2 groups after
52 weeks and a cost-benefit analysis conducted.
;
Allocation: Randomized, Intervention Model: Parallel Assignment, Masking: Open Label
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